Methods and systems for flight control for managing actuators for an electric aircraft

ABSTRACT

A system for flight control for managing actuators for an electric aircraft is provided. The system includes a controller, wherein the controller is designed and configured to receive a sensor datum from at least a sensor, generate an actuator performance model as a function of the sensor datum, identify a defunct actuator of the electric aircraft as a function of the sensor datum and the actuator performance model, generate an actuator allocation command datum as a function of at least the actuator performance model and at least the identification of the defunct actuator, and perform a torque allocation as a function of the actuator allocation command datum.

FIELD OF THE INVENTION

The present invention generally relates to the field of flight control.In particular, the present invention is directed to methods system forflight control for managing actuators for an electric aircraft.

BACKGROUND

In electrically propelled vehicles, such as an electric vertical takeoffand landing (eVTOL) aircraft, it is essential to maintain the integrityof the aircraft until safe landing. It is also essential for theaircraft and its flight components to work in tandem to compensate for amalfunction or failure with one or more of its flight components. Insome flights, a component of the aircraft may experience a malfunctionor failure which will put the aircraft in an unsafe mode which willcompromise the safety of the aircraft, passengers and onboard cargo.

SUMMARY OF THE DISCLOSURE

In an aspect a system for flight control for managing actuators for anelectric aircraft is provided. The system includes a controller, whereinthe controller is designed and configured to receive a sensor datum fromat least a sensor, generate an actuator performance model as a functionof the sensor datum, identify a defunct actuator of the electricaircraft as a function of the sensor datum and the actuator performancemodel, generate an actuator allocation command datum as a function of atleast the actuator performance model and at least the identification ofthe defunct actuator, and perform a torque allocation as a function ofthe actuator allocation command datum.

In another aspect a method for flight control for managing actuators foran electric aircraft is provided. The method includes receiving, by acontroller, a sensor datum from at least a sensor, generating anactuator performance model as a function of the sensor datum,identifying a defunct actuator of the electric aircraft as a function ofthe sensor datum and the actuator performance model, generating anactuator allocation command datum as a function of at least the actuatorperformance model and at least the identification of the defunctactuator, and performing a torque allocation as a function of theactuator allocation command datum.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a system forflight control for managing actuators for an electric aircraft;

FIG. 2 is an illustrative embodiment of an outer loop controller for usein embodiments of the present invention;

FIG. 3 is an illustrative embodiment of an inner loop controller for usein embodiments of the present invention;

FIG. 4 is a flow diagram of an exemplary method for flight control formanaging actuators for an electric aircraft;

FIG. 5 is an illustration of an exemplary embodiment of an electricaircraft;

FIG. 6 is a block diagram of an exemplary flight controller;

FIG. 7 is a block diagram of an exemplary machine-learning process;

FIG. 8 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims. For purposes ofdescription herein, the terms “upper”, “lower”, “left”, “rear”, “right”,“front”, “vertical”, “horizontal”, and derivatives thereof shall relateto embodiments oriented as shown for exemplary purposes in FIG. 6.Furthermore, there is no intention to be bound by any expressed orimplied theory presented in the preceding technical field, background,brief summary or the following detailed description. It is also to beunderstood that the specific devices and processes illustrated in theattached drawings, and described in the following specification, aresimply embodiments of the inventive concepts defined in the appendedclaims. Hence, specific dimensions and other physical characteristicsrelating to the embodiments disclosed herein are not to be considered aslimiting, unless the claims expressly state otherwise.

At a high level, aspects of the present disclosure are directed tosystems and methods for flight control for managing actuators for anelectric aircraft. In an embodiment, aspects of the present disclosureinclude a controller, wherein the controller is designed and configuredto receive a sensor datum from at least a sensor, generate an actuatorperformance model as a function of the sensor datum, identify a defunctactuator of the electric aircraft as a function of the sensor datum andthe actuator performance model, generate an actuator allocation commanddatum as a function of at least the actuator performance model and atleast the identification of the defunct actuator, and perform a torqueallocation as a function of the actuator allocation command datum.Exemplary embodiments illustrating aspects of the present disclosure aredescribed below in the context of several specific examples

Aspects of the present disclosure can be used to delineate torque outputin the instance of a malfunction or failure of a flight component. Forexample and without limitation, aspects of the present disclosure canallocate more torque to remaining flight components to compensate forthe malfunctioning flight component to maintain uninterrupted flight.Aspects of the present disclosure can be used to automatically allocatetorque to aircraft flight components or actuators. Aspects of thepresent disclosure can also be used to inform a pilot of the aircraft ofthe malfunctioning flight component or actuators in which the pilot maymanually operate the aircraft to the pilots satisfaction.

Aspects of the present disclosure can be used as a vehicle failure modethat indicates one or more specific actuators of a vehicle aremalfunctioning. For example, one failure mode comprises a front leftrotor failure whereas another failure mode comprises a back right rotorfailure. A vehicle failure mode may indicate a group of failed actuators(e.g., where one of the actuators in the group is believed to havefailed but is not specifically identified). In some embodiments,multiple models of the vehicle are determined based on multiple vehiclefailure modes. An expected attitude and expected attitude rate may bedetermined based on each model. For example, expected attitude andattitude rates based on different actuator failures are determined. Insome embodiments, the multiple expected attitudes and expected attituderates are compared to an actual attitude and attitude rate as observedby an inertial measurement unit. In the event the actual values match orapproximately match expected values of a model, a corresponding failuremode of the model may be determined to be in effect. For example, theone or more actuators indicated by the failure mode are determined to bemalfunctioning actuators.

Aspects of the present disclosure can also be used to combines use ofsensors and model-based estimation to provide a robust, lightweight, andinexpensive means to monitor and respond to actuator failures. Typicalsolutions may require speed controllers, sensors, or gauges designed tospecifically measure output or function of actuators. Aspects of thepresent disclosure may measure whether the desired effect of theactuators is occurring by using high-level models rather than measuringa direct output of the actuators. Detecting high-level performance mayprovide greater accuracy. For example, a motor speed detector providesinformation on the motor but is unaware of a broken propeller, whereas ahigh-level model catches that the vehicle is not moving in a directionit was commanded to. Utilizing an existing inertial measurement unit todetermine actuator operability in lieu of additional equipment mayprovide a lightweight solution in vehicles such as aircraft that haveweight restrictions. Cost and complexity due to specialized monitoringequipment or repairs to actuator output monitoring equipment iseliminated.

Aspects of the present disclosure can be used to quickly detect faultsin the vehicle and automatically perform responsive actions. Responsiveactions may comprise corrective actions, such as adjusting flightcontrols, or warning actions, such as providing information to a vehicleoperator.

Referring now to FIG. 1, a block diagram of an exemplary embodiment of asystem 100 for flight control for managing actuators for an electricaircraft is illustrated. System 100 includes controller 112. Controller112 may include a flight controller. Controller 112 may include acomputing device. computing device may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. computing device may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. computingdevice may interface or communicate with one or more additional devicesas described below in further detail via a network interface device.Network interface device may be utilized for connecting computing deviceto one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.computing device may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. computing device may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. computing device may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. computing device may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 100and/or computing device.

With continued reference to FIG. 1, computing device may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing devicemay be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. computing device mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 controller 112 and/or flightcontroller may be controlled by one or moreProportional-Integral-Derivative (PID) algorithms driven, for instanceand without limitation by stick, rudder and/or thrust control lever withanalog to digital conversion for fly by wire as described herein andrelated applications incorporated herein by reference. A “PIDcontroller”, for the purposes of this disclosure, is a control loopmechanism employing feedback that calculates an error value as thedifference between a desired setpoint and a measured process variableand applies a correction based on proportional, integral, and derivativeterms; integral and derivative terms may be generated, respectively,using analog integrators and differentiators constructed withoperational amplifiers and/or digital integrators and differentiators,as a non-limiting example. A similar philosophy to attachment of flightcontrol systems to sticks or other manual controls via pushrods and wiremay be employed except the conventional surface servos, steppers, orother electromechanical actuator components may be connected to thecockpit inceptors via electrical wires. Fly-by-wire systems may bebeneficial when considering the physical size of the aircraft, utilityof for fly by wire for quad lift control and may be used for remote andautonomous use, consistent with the entirety of this disclosure.Controller 112 may harmonize vehicle flight dynamics with best handlingqualities utilizing the minimum amount of complexity whether it beadditional modes, augmentation, or external sensors as described herein.

With continued reference to FIG. 1, controller 112 is configured toreceive sensor datum 108 from at least a sensor. A “sensor,” for thepurposes of this disclosure, is an electronic device configured todetect, capture, measure, or combination thereof, one or more elementsof data describing external and/or electric vehicle conditions. Sensor104 may be integrated and/or connected to at least an actuator, aportion thereof, or any subcomponent thereof. Sensor 104 may include aphotodiode configured to convert light, heat, electromagnetic elements,and the like thereof, into electrical current for further analysisand/or manipulation. Sensor 104 may include circuitry or electroniccomponents configured to digitize, transform, or otherwise manipulateelectrical signals. Electrical signals may include analog signals,digital signals, periodic or aperiodic signal, step signals, unitimpulse signal, unit ramp signal, unit parabolic signal, signumfunction, exponential signal, rectangular signal, triangular signal,sinusoidal signal, sinc function, or pulse width modulated signal. Theplurality of datum captured by sensor 104 may include circuitry,computing devices, electronic components or a combination thereof thattranslates into at least an electronic signal configured to betransmitted to another electronic component. Sensor 104 may be disposedon at least an actuator of the electric aircraft. An “actuator,” for thepurpose of this disclosure, is any flight component or any part of anelectric aircraft that helps it to achieve physical movements byconverting energy, often electrical, air, or hydraulic, into mechanicalforce and enable movement. In a non-limiting embodiment, the at least anactuator may include, but not limited to, pistons, forward pushers,vertical propulsors, motors, rotors, ailerons, rudders, and the likethereof. “Disposed,” for the purpose of this disclosure, is the physicalplacement of a computing device on an actuator. In a non-limitingembodiment, actuator may include a flight component. In a non-limitingembodiment, sensor 104 may include a plurality of individual sensorsdisposed on each actuator of the electric aircraft.

With continued reference to FIG. 1, sensor 104 may include a motionsensor. A “motion sensor”, for the purposes of this disclosure is adevice or component configured to detect physical movement of an objector grouping of objects. One of ordinary skill in the art wouldappreciate, after reviewing the entirety of this disclosure, that motionmay include a plurality of types including but not limited to: spinning,rotating, oscillating, gyrating, jumping, sliding, reciprocating, or thelike. Sensor 104 may include, but not limited to, torque sensor,gyroscope, accelerometer, magnetometer, inertial measurement unit (IMU),pressure sensor, force sensor, proximity sensor, displacement sensor,vibration sensor, LIDAR sensor, and the like. In a non-limitingembodiment sensor 104 ranges may include a technique for the measuringof distances or slant range from an observer including sensor 104 to atarget which may include a plurality of outside parameters. “Outsideparameter,” for the purposes of this disclosure, refer to environmentalfactors or physical electric vehicle factors including health statusthat may be further be captured by a sensor 104. Outside parameter mayinclude, but not limited to air density, air speed, true airspeed,relative airspeed, temperature, humidity level, and weather conditions,among others. Outside parameter may include velocity and/or speed in aplurality of ranges and direction such as vertical speed, horizontalspeed, changes in angle or rates of change in angles like pitch rate,roll rate, yaw rate, or a combination thereof, among others. Outsideparameter may further include physical factors of the components of theelectric aircraft itself including, but not limited to, remaining fuelor battery. Outside parameter may include at least an environmentalparameter. Environmental parameter may be any environmentally basedperformance parameter as disclosed herein. Environment parameter mayinclude, without limitation, time, pressure, temperature, air density,altitude, gravity, humidity level, airspeed, angle of attack, anddebris, among others. Environmental parameters may be stored in anysuitable datastore consistent with this disclosure. Environmentalparameters may include latitude and longitude, as well as any otherenvironmental condition that may affect the landing of an electricaircraft. Technique may include the use of active range finding methodswhich may include, but not limited to, light detection and ranging(LIDAR), radar, sonar, ultrasonic range finding, and the like. In anon-limiting embodiment, sensor 104 may include at least a LIDAR systemto measure ranges including variable distances from sensor 104 to apotential landing zone or flight path. LIDAR systems may include, butnot limited to, a laser, at least a phased array, at least amicroelectromechanical machine, at least a scanner and/or optic, aphotodetector, a specialized GPS receiver, and the like. In anon-limiting embodiment, sensor 104 including a LIDAR system may targetan object with a laser and measure the time for at least a reflectedlight to return to the LIDAR system. LIDAR may also be used to makedigital 4-D representations of areas on the earth's surface and oceanbottom, due to differences in laser return times, and by varying laserwavelengths. In a non-limiting embodiment the LIDAR system may include atopographic LIDAR and a bathymetric LIDAR, wherein the topographic LIDARthat may use near-infrared laser to map a plot of a land or surfacerepresenting a potential landing zone or potential flight path while thebathymetric LIDAR may use water-penetrating green light to measureseafloor and various water level elevations within and/or surroundingthe potential landing zone. In a non-limiting embodiment, electricaircraft may use at least a LIDAR system as a means of obstacledetection and avoidance to navigate safely through environments to reacha potential landing zone. Sensor 104 may include a sensor suite whichmay include a plurality of sensors that may detect similar or uniquephenomena. For example, in a non-limiting embodiment, sensor suite mayinclude a plurality of accelerometers, a mixture of accelerometers andgyroscopes, or a mixture of an accelerometer, gyroscope, and torquesensor.

With continued reference to FIG. 1, sensor 104 may be mechanically andcommunicatively connected to one or more throttles. “Mechanicallyconnected,” for the purpose of this disclosure, is a connection in whichan electrical device is directly connected to another electrical devicein which the connection is configured to support the transfer of torque.The throttle may be any throttle as described herein, and innon-limiting examples, may include pedals, sticks, levers, buttons,dials, touch screens, one or more computing devices, and the like.Additionally, a right-hand floor-mounted lift lever may be used tocontrol the amount of thrust provided by the lift fans or otherpropulsors. The rotation of a thumb wheel pusher throttle may be mountedon the end of this lever and may control the amount of torque providedby the pusher motor, or one or more other propulsors, alone or incombination. Any throttle as described herein may be consistent with anythrottle described in U.S. patent application Ser. No. 16/929,206 andtitled, “Hover and Thrust Control Assembly for Dual-Mode Aircraft”,which is incorporated herein in its entirety by reference. At least asensor 104 may be mechanically and communicatively connected to aninceptor stick. The pilot input may include a left-hand strain-gaugestyle STICK for the control of roll, pitch and yaw in both forward andassisted lift flight. A 4-way hat switch on top of the left-hand stickenables the pilot to set roll and pitch trim. Any inceptor stickdescribed herein may be consistent with any inceptor or directionalcontrol as described in U.S. patent application Ser. No. 17/001,845 andtitled, “A Hover and Thrust Control Assembly for a Dual-Mode Aircraft”,which is incorporated herein in its entirety by reference.

Referring still to FIG. 1, at least a sensor 104 may be mechanically andcommunicatively connected to a foot pedal. Flight control system 104 mayincorporate wheeled landing gear steerable by differential brakingaccessed by floor mounted pedals; in the event of installing such a footactuated “caveman” infrastructure, yaw control also may be affectedthrough differential foot pressure. A stick may be calibrated at zeroinput (relaxed state) and at the stops in pitch and roll. Thecalibration may be done in both directions of roll and both directionsof pitch. Any asymmetries may be handled by a bilinear calibration withthe breakpoint at the neutral point. Likewise, a yaw zero point maycorrespond to a relaxed state of an inceptor stick. The full-scaletorque in each twist direction may be independently calibrated to themaximum torque seen in the calibration process in that direction. In allphases of flight, the control surface deflections may be linearly mappedto their corresponding maximum stick deflections and neutral position.In the case of roll, where there may be more aileron deflection in thetrailing edge up direction, the degrees of deflection per pilot inputunit may be different in each direction, such that full surfacedeflection may be not reached until full stick deflection. When the liftfans are engaged, the pilot's stick inputs may correspond to roll andpitch attitude (+/−30 deg) and yaw rate (+/−60 deg/second) commands,which are also linearly mapped to the full range of stick travel. Abreakout force of 2-3 Newtons (0.5 lbf minimums mil spec 1797 minbreakout force) measured at center of stick grip position may be appliedprior to the linear mapping. Breakout force prevents adverse roll yawcoupling. In order to remove the need for constant control input insteady forward flight, pitch and roll trim may be available. Pitch trimmay be limited to +7 deg pitch up trim and −5 deg pitch down trim, whichmay be sufficient to trim for level flight over the entire center ofgravity and cruise airspeed range in non-limiting examples. Roll trimlimited to 2 degrees (average between the ailerons) may be alsoavailable. The trim may be applied after the breakout force to changethe input that center stick corresponds to. This trimmed command appliesto both the attitude commands when the lift rotors are powered, and thecontrol surface deflections at all times. In order to ensure the pilotcan always access the full capability of the aircraft, the mapping belowfrom pre-trim input to post-trim input may be used when trim is nonzero.Note that with positive trim, the effective sensitivity in the positivedirection has decreased while the sensitivity in the negative directionhas increased. This is a necessary byproduct of enforcing the constraintthat full stick deflection yields full control surface deflection. Thelift lever has very low additional breakout torque and requires aconstant (but adjustable) torque of 3.1 Nm during movement, whichtranslates to 2 lbf at the intended grip position. Control of the liftmotors may be only active when the assisted lift lever may be raisedabove 3.75 degrees from the full down stop (out of 25 degrees total).This may represent a debounce mechanism that may be determined based onthe friction of the assisted lift lever, the mass and the expectedcockpit vibration levels. A mechanical detent may be installed on thelift lever at an angle corresponding to 15% average torque in order toprovide kinesthetic feedback to the pilot of the minimum lift leversetting which provides adequate control authority via the lift fans.

With continued reference to FIG. 1, flight control system 100 mayinclude at least a sensor 104 which may further include a sensor suite.One or more sensors may be communicatively connected to at least a pilotcontrol, the manipulation of which, may constitute at least an aircraftcommand. “Communicative connecting”, for the purposes of thisdisclosure, refers to two or more components electrically, or otherwiseconnected and configured to transmit and receive signals from oneanother. Signals may include electrical, electromagnetic, visual, audio,radio waves, or another undisclosed signal type alone or in combination.Any datum or signal herein may include an electrical signal. Electricalsignals may include analog signals, digital signals, periodic oraperiodic signal, step signals, unit impulse signal, unit ramp signal,unit parabolic signal, signum function, exponential signal, rectangularsignal, triangular signal, sinusoidal signal, sinc function, or pulsewidth modulated signal. At least a sensor 104 may include circuitry,computing devices, electronic components or a combination thereof thattranslates input datum 108 into at least an electronic signal configuredto be transmitted to another electronic component. At least a sensorcommunicatively connected to at least a pilot control may include asensor disposed on, near, around or within at least pilot control. Atleast a sensor may include a motion sensor. “Motion sensor”, for thepurposes of this disclosure refers to a device or component configuredto detect physical movement of an object or grouping of objects. One ofordinary skill in the art would appreciate, after reviewing the entiretyof this disclosure, that motion may include a plurality of typesincluding but not limited to: spinning, rotating, oscillating, gyrating,jumping, sliding, reciprocating, or the like. At least a sensor mayinclude, torque sensor, gyroscope, accelerometer, torque sensor,magnetometer, inertial measurement unit (IMU), pressure sensor, forcesensor, proximity sensor, displacement sensor, vibration sensor, amongothers. At least a sensor 104 may include a sensor suite which mayinclude a plurality of sensors that may detect similar or uniquephenomena. For example, in a non-limiting embodiment, sensor suite mayinclude a plurality of accelerometers, a mixture of accelerometers andgyroscopes, or a mixture of an accelerometer, gyroscope, and torquesensor.

Still referring to FIG. 1, sensor 104 may include a plurality of sensorsin the form of individual sensors or a sensor suite working in tandem orindividually. A sensor suite may include a plurality of independentsensors, as described herein, where any number of the described sensorsmay be used to detect any number of physical or electrical quantitiesassociated with an aircraft power system or an electrical energy storagesystem. Independent sensors may include separate sensors measuringphysical or electrical quantities that may be powered by and/or incommunication with circuits independently, where each may signal sensoroutput to a control circuit such as a user graphical interface. In anembodiment, use of a plurality of independent sensors may result inredundancy configured to employ more than one sensor that measures thesame phenomenon, those sensors being of the same type, a combination of,or another type of sensor not disclosed, so that in the event one sensorfails, the ability to detect phenomenon is maintained and in anon-limiting example, a user alter aircraft usage pursuant to sensorreadings. At least a sensor may be configured to detect pilot input fromat least pilot control. At least pilot control may include a throttlelever, inceptor stick, collective pitch control, steering wheel, brakepedals, pedal controls, toggles, joystick. One of ordinary skill in theart, upon reading the entirety of this disclosure would appreciate thevariety of pilot input controls that may be present in an electricaircraft consistent with the present disclosure. Inceptor stick may beconsistent with disclosure of inceptor stick in U.S. patent applicationSer. No. 17/001,845 and titled “A HOVER AND THRUST CONTROL ASSEMBLY FORDUAL-MODE AIRCRAFT”, which is incorporated herein by reference in itsentirety. Collective pitch control may be consistent with disclosure ofcollective pitch control in U.S. patent application Ser. No. 16/929,206and titled “HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”,which is incorporated herein by reference in its entirety.

Further referring to FIG. 1, at least pilot control may be physicallylocated in the cockpit of the aircraft or remotely located outside ofthe aircraft in another location communicatively connected to at least aportion of the aircraft. “Communicatively connection”, for the purposesof this disclosure, is a process whereby one device, component, orcircuit is able to receive data from and/or transmit data to anotherdevice, component, or circuit; communicative connecting may be performedby wired or wireless electronic communication, either directly or by wayof one or more intervening devices or components. In an embodiment,communicative connecting includes electrically coupling an output of onedevice, component, or circuit to an input of another device, component,or circuit. Communicative connecting may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative connecting may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical coupling, or the like.At least pilot control may include buttons, switches, or other binaryinputs in addition to, or alternatively than digital controls aboutwhich a plurality of inputs may be received. At least pilot control maybe configured to receive pilot input. Pilot input may include a physicalmanipulation of a control like a pilot using a hand and arm to push orpull a lever, or a pilot using a finger to manipulate a switch. Pilotinput may include a voice command by a pilot to a microphone andcomputing system consistent with the entirety of this disclosure. One ofordinary skill in the art, after reviewing the entirety of thisdisclosure, would appreciate that this is a non-exhaustive list ofcomponents and interactions thereof that may include, represent, orconstitute, or be connected to sensor 104.

In an embodiment, and still referring to FIG. 1, sensor 104 may beattached to one or more pilot inputs and attached to one or more pilotinputs, one or more portions of an aircraft, and/or one or morestructural components, which may include any portion of an aircraft asdescribed in this disclosure. As used herein, a person of ordinary skillin the art would understand “attached” to mean that at least a portionof a device, component, or circuit is connected to at least a portion ofthe aircraft via a mechanical connection. Said mechanical connection caninclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling can be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling can be used to join two pieces ofrotating electric aircraft components. Control surfaces may each includeany portion of an aircraft that can be moved or adjusted to affectaltitude, airspeed velocity, groundspeed velocity or direction duringflight. For example, control surfaces may include a component used toaffect the aircrafts' roll and pitch which may comprise one or moreailerons, defined herein as hinged surfaces which form part of thetrailing edge of each wing in a fixed wing aircraft, and which may bemoved via mechanical means such as without limitation servomotors,mechanical linkages, or the like, to name a few. As a further example,control surfaces may include a rudder, which may include, withoutlimitation, a segmented rudder. The rudder may function, withoutlimitation, to control yaw of an aircraft. Also, control surfaces mayinclude other flight control surfaces such as propulsors, rotatingflight controls, or any other structural features which can adjust themovement of the aircraft. A “control surface” as described herein, isany form of a mechanical linkage with a surface area that interacts withforces to move an aircraft. A control surface may include, as anon-limiting example, ailerons, flaps, leading edge flaps, rudders,elevators, spoilers, slats, blades, stabilizers, stabilators, airfoils,a combination thereof, or any other mechanical surface are used tocontrol an aircraft in a fluid medium. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousmechanical linkages that may be used as a control surface, as used anddescribed in this disclosure.

With continued reference to FIG. 1, controller 112 is configured toreceive sensor datum 108 from sensor 104 in which sensor 104 may beconfigured to detect sensor datum 108. A “sensor datum,” for the purposeof this disclosure, is any datum or element of data describingparameters captured by a sensor describing the outside environment andphysical values describing the performance or qualities of flightcomponents of the electric aircraft. In a non-limiting embodiment,sensor datum 108 may include any data captured by any sensor asdescribed in the entirety of this disclosure. Additionally andalternatively, sensor datum 108 may include any element or signal ofdata that represents an electric aircraft route and variousenvironmental or outside parameters. In a non-limiting embodiment,sensor datum may include an element of that representing the safest,most efficient, shortest, or a combination thereof, flight path. In anon-limiting embodiment, sensor datum 108 may include a degree of torquethat may be sensed, without limitation, using load sensors deployed atand/or around a propulsor and/or by measuring back electromotive force(back EMF) generated by a motor driving the propulsor. In an embodiment,use of a plurality of independent sensors may result in redundancyconfigured to employ more than one sensor that measures the samephenomenon, those sensors being of the same type, a combination of, oranother type of sensor not disclosed, so that in the event one sensorfails, the ability to detect phenomenon is maintained and in anon-limiting example, a user alter aircraft usage pursuant to sensorreadings. One of ordinary skill in the art will appreciate, afterreviewing the entirety of this disclosure, that motion may include aplurality of types including but not limited to: spinning, rotating,oscillating, gyrating, jumping, sliding, reciprocating, or the like.

With continued reference to FIG. 1, sensor datum 108 may include aninput datum. An “input datum,” for the purpose of this disclosure, is anelement of data describing a manipulation of one or more pilot inputcontrols that correspond to a desire to affect an aircraft's trajectoryas a function of the movement of one or more flight components and/oractuators. At least a pilot control may be communicatively connected toany other component presented in system, the communicative connectionmay include redundant connections configured to safeguard againstsingle-point failure. Pilot input may indicate a pilot's desire tochange the heading or trim of an electric aircraft. Pilot input mayindicate a pilot's desire to change an aircraft's pitch, roll, yaw, orthrottle. Aircraft trajectory is manipulated by one or more controlsurfaces and propulsors working alone or in tandem consistent with theentirety of this disclosure, hereinbelow. Pitch, roll, and yaw may beused to describe an aircraft's attitude and/or heading, as theycorrespond to three separate and distinct axes about which the aircraftmay rotate with an applied moment, torque, and/or other force applied toat least a portion of an aircraft. “Pitch”, for the purposes of thisdisclosure refers to an aircraft's angle of attack, that is thedifference between the aircraft's nose and the horizontal flighttrajectory. For example, an aircraft pitches “up” when its nose isangled upward compared to horizontal flight, like in a climb maneuver.In another example, the aircraft pitches “down”, when its nose is angleddownward compared to horizontal flight, like in a dive maneuver. Whenangle of attack is not an acceptable input to any system disclosedherein, proxies may be used such as pilot controls, remote controls, orsensor levels, such as true airspeed sensors, pitot tubes,pneumatic/hydraulic sensors, and the like. “Roll” for the purposes ofthis disclosure, refers to an aircraft's position about its longitudinalaxis, that is to say that when an aircraft rotates about its axis fromits tail to its nose, and one side rolls upward, like in a bankingmaneuver. “Yaw”, for the purposes of this disclosure, refers to anaircraft's turn angle, when an aircraft rotates about an imaginaryvertical axis intersecting the center of the earth and the fuselage ofthe aircraft. “Throttle”, for the purposes of this disclosure, refers toan aircraft outputting an amount of thrust from a propulsor. Pilotinput, when referring to throttle, may refer to a pilot's desire toincrease or decrease thrust produced by at least a propulsor. In anon-limiting embodiment, input datum may include an electrical signal.In a non-limiting embodiment, input datum may include mechanicalmovement of any throttle consistent with the entirety of thisdisclosure. Electrical signals may include analog signals, digitalsignals, periodic or aperiodic signal, step signals, unit impulsesignal, unit ramp signal, unit parabolic signal, signum function,exponential signal, rectangular signal, triangular signal, sinusoidalsignal, sinc function, or pulse width modulated signal. At least asensor may include circuitry, computing devices, electronic componentsor a combination thereof that translates pilot input into at input datumconfigured to be transmitted to any other electronic component.

With continued reference to FIG. 1, sensor datum 108 may include aflight datum. A “flight datum,” for the purpose of this disclosure, isany datum or element of data describing physical parameters ofindividual actuators and/or flight components of an electric aircraftand/or logistical parameters of the electric aircraft. In a non-limitingembodiment, flight datum may include a plurality of data describing thehealth status of an actuator of a plurality of actuators. In anon-limiting embodiment, the plurality of data may include a pluralityof failure data for a plurality of actuators. In a non-limitingembodiment, safety datum may include a measured torque parameter thatmay include the remaining vehicle torque of a flight component among aplurality of flight components. A “measured torque parameter,” for thepurposes of this disclosure, refer to a collection of physical valuesrepresenting a rotational equivalence of linear force. A person ofordinary skill in the art, after viewing the entirety of thisdisclosure, would appreciate the various physical factors in measuringtorque of an object. For instance and without limitation, remainingvehicle torque may be consistent with disclosure of remaining vehicletorque in U.S. patent application Ser. No. 17/197,427 and titled “SYSTEMAND METHOD FOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT”, which isincorporated herein by reference in its entirety. Remaining vehicletorque may include torque available at each of a plurality of flightcomponents at any point during an aircraft's entire flight envelope,such as before, during, or after a maneuver. For example, and withoutlimitation, torque output may indicate torque a flight component mustoutput to accomplish a maneuver; remaining vehicle torque may then becalculated based on one or more of flight component limits, vehicletorque limits, environmental limits, or a combination thereof. Vehicletorque limit may include one or more elements of data representingmaxima, minima, or other limits on vehicle torques, forces, attitudes,rates of change, or a combination thereof. Vehicle torque limit mayinclude individual limits on one or more flight components, structuralstress or strain, energy consumption limits, or a combination thereof.Remaining vehicle torque may be represented, as a non-limiting example,as a total torque available at an aircraft level, such as the remainingtorque available in any plane of motion or attitude component such aspitch torque, roll torque, yaw torque, and/or lift torque. In anon-limiting embodiment, controller 112 may mix, refine, adjust,redirect, combine, separate, or perform other types of signal operationsto translate pilot desired trajectory into aircraft maneuvers. In anonlimiting embodiment a pilot may send a pilot input at a press of abutton to capture current states of the outside environment andsubsystems of the electric aircraft to be displayed onto an outputdevice in pilot view. The captured current state may further display anew focal point based on that captured current state. In a non-limitingembodiment, controller 112 may condition signals such that they can besent and received by various components throughout the electric vehicle.In a non-limiting embodiment, flight datum may include at least anaircraft angle. At least an aircraft angle may include any informationabout the orientation of the aircraft in three-dimensional space such aspitch angle, roll angle, yaw angle, or some combination thereof. Innon-limiting examples, at least an aircraft angle may use one or morenotations or angular measurement systems like polar coordinates,cartesian coordinates, cylindrical coordinates, spherical coordinates,homogenous coordinates, relativistic coordinates, or a combinationthereof, among others. In a non-limiting embodiment, flight datum mayinclude at least an aircraft angle rate. At least an aircraft angle ratemay include any information about the rate of change of any angleassociated with an electrical aircraft as described herein. Anymeasurement system may be used in the description of at least anaircraft angle rate.

With continued reference to FIG. 1, controller 112 is configured toreceive sensor datum 108 from sensor 104. In a non-limiting embodiment,controller 112 may include a plurality of physical controller areanetwork buses communicatively connected to the aircraft and sensor 104.A “physical controller area network bus,” as used in this disclosure, isvehicle bus unit including a central processing unit (CPU), a CANcontroller, and a transceiver designed to allow devices to communicatewith each other's applications without the need of a host computer whichis located physically at the aircraft. Physical controller area network(CAN) bus unit may include physical circuit elements that may use, forinstance and without limitation, twisted pair, digital circuitelements/FGPA, microcontroller, or the like to perform, withoutlimitation, processing and/or signal transmission processes and/ortasks. For instance and without limitation, CAN bus unit may beconsistent with disclosure of CAN bus unit in U.S. patent applicationSer. No. 17/218,342 and titled “METHOD AND SYSTEM FOR VIRTUALIZING APLURALITY OF CONTROLLER AREA NETWORK BUS UNITS COMMUNICATIVELY CONNECTEDTO AN AIRCRAFT,” which is incorporated herein by reference in itsentirety. In a non-limiting embodiment, the controller 112 may receivethe sensor datum 108 from the sensor 104 by a physical CAN bus unit. Ina non-limiting embodiment, the sensor 104 may include a physical CAN busunit to detect sensor datum 108 in tandem with a plurality of individualsensors from a sensor suite. Physical CAN bus unit may include multiplexelectrical wiring for transmission of multiplexed signaling. PhysicalCAN bus unit 104 may include message-based protocol(s), wherein theinvoking program sends a message to a process and relies on that processand its supporting infrastructure to then select and run appropriateprograming. A plurality of physical CAN bus units may be locatedphysically at the aircraft may include mechanical connection to theaircraft, wherein the hardware of the physical CAN bus unit isintegrated within the infrastructure of the aircraft.

In a non-limiting embodiment, controller 112 may be responsible only formapping the pilot inputs such as input datum, attitude such as at leastan aircraft angle, and body angular rate measurement such as at least anaircraft angle rate to motor torque levels necessary to meet the inputdatum. In a non-limiting exemplary embodiment, controller 112 mayinclude the nominal attitude command (ACAH) configuration, thecontroller 112 may make the vehicle attitude track the pilot attitudewhile also applying the pilot-commanded amount of assisted lift andpusher torque which may be encapsulated within actuator allocationcommand datum 152. The flight controller is responsible only for mappingthe pilot inputs, attitude, and body angular rate measurements to motortorque levels necessary to meet the input datum. In the nominal attitudecommand (ACAH) configuration, controller 112 makes the vehicle attitudetrack the pilot attitude while also applying the pilot commanded amountof assisted lift and pusher torque. In a non-limiting embodiment,controller 112 may include the calculation and control of avionicsdisplay of critical envelope information i.e., stall warning, vortexring state, pitch limit indicator, angle of attack, transitionenvelopes, etc. In a non-limiting embodiment, controller 112 maycalculate, command, and control trim assist, turn coordination, pitch tocertain gravitational forces, automation integration: attitude, positionhold, LNAV, VNAV etc., minimum hover thrust protection, angle of attacklimits, etc., precision Autoland, other aspects of autopilot operations,advanced perception of obstacles for ‘see and avoid’ missions, andremote operations, among others.

With continued reference to FIG. 1, controller 112 is configured togenerate actuator performance model 120 as a function of the sensordatum 108. An “actuator performance model,” for the purpose of thisdisclosure, is an analytical and/or interactive visualization and/ormathematical model regarding aircraft operation and/or performancecapabilities. In a non-limiting embodiment, actuator performance model124 may include a model depicting the performance of the aircraft inwhich one or more of the actuators are malfunctioning or failing. In anon-limiting embodiment, actuator performance model 124 may be generatedduring a flight or after a flight has occurred. For example and withoutlimitation, actuator performance model 124 may depict the performance ofthe aircraft and the aircraft actuators in real time as it is flying inthe air. In a non-limiting embodiment, actuator performance model 124may include a depiction of the flight of the aircraft. In a non-limitingembodiment, actuator performance model 124 may include a plurality ofperformance parameters include, but not limited to, aircraft velocity,attitude, actuator torque output, and the like thereof. In anon-limiting embodiment, actuator performance model 124 may highlight anabnormality of an actuator and a plurality of performance parametersassociated with that abnormal actuator. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of the variousembodiments of a simulation and/or model in the context of visualizationand analysis consistent with this disclosure.

With continued reference to FIG. 1, controller 112 may include flightsimulator 116, wherein the flight simulator may be configured togenerate actuator performance model 124. A “flight simulator” is aprogram or set of operations that simulate flight. In some cases, flightsimulator may simulate flight within an environment, for example anenvironmental atmosphere in which aircraft fly, airports at whichaircraft take-off and land, and/or mountains and other hazards aircraftattempt to avoid crashing into. For instance and without limitation,flight simulator may be consistent with flight simulator in U.S. patentapplication Ser. No. 17/348,916 and titled “METHODS AND SYSTEMS FORSIMULATED OPERATION OF AN ELECTRIC VERTICAL TAKE-OFF AND LANDING (EVTOL)AIRCRAFT,” which is incorporated herein by reference in its entirety. Insome cases, an environment may include geographical, atmospheric, and/orbiological features. In some cases, flight simulator 116 may model anartificial and/or virtual aircraft in flight as well as an environmentin which the artificial and/or virtual aircraft flies. In some cases,flight simulator 116 may include one or more physics models, whichrepresent analytically or through data-based, such as without limitationmachine-learning processes, physical phenomenon. Physical phenomenon maybe associated with an aircraft and/or an environment. For example, someversions of flight simulator 116 may include thermal models representingaircraft components by way of thermal modeling. Thermal modelingtechniques may, in some cases, include analytical representation of oneor more of convective hear transfer (for example by way of Newton's Lawof Cooling), conductive heat transfer (for example by way of Fourierconduction), radiative heat transfer, and/or advective heat transfer. Insome cases, flight simulator 116 may include models representing fluiddynamics. For example, in some embodiments, flight simulator may includea representation of turbulence, wind shear, air density, cloud,precipitation, and the like. In some embodiments, flight simulator 116may include at least a model representing optical phenomenon. Forexample, flight simulator 116 may include optical models representativeof transmission, reflectance, occlusion, absorption, attenuation, andscatter. Flight simulator 116 may include non-analytical modelingmethods; for example, the flight simulator may include, withoutlimitation, a Monte Carlo model for simulating optical scatter within aturbid medium, for example clouds. In some embodiments, flight simulator116 may represent Newtonian physics, for example motion, pressures,forces, moments, and the like. An exemplary flight simulator may includeMicrosoft Flight Simulator from Microsoft of Redmond, Wash., U.S.A.

With continued reference to FIG. 1, flight simulator 116 may beconfigured to generate expected actuator performance model 124. An“expected actuator performance model,” for the purpose of thisdisclosure, is any actuator performance model of the aircraft thatembodies an ideal or expected analytical and/or interactivevisualization regarding aircraft operation and/or performancecapabilities. In a non-limiting embodiment, expected actuatorperformance model 124 may include any actuator performance modelsimulating a plurality of actuator parameters operating within apredetermined tolerance. For example and without limitation, thepredetermined tolerance may include upper and lower limits for aplurality of actuator parameters such as, but not limited to, angle ofattack, attitude distance, rotor output, and the like thereof, in whichexpected actuator performance model 124 may be configured to simulate.In a non-limiting embodiment, expected actuator performance model 124may include an actuator performance model that depicts a performancemodel in which none of the actuators are malfunctioning. For example andwithout limitation, expected actuator performance model 124 may be amodel depicting a performance of what the aircraft should be based onthe ideal, expected, or initial performance the aircraft actuators areintended to perform. For example and without limitation, expectedactuator performance model 124 includes peak performance outputincluding, but not limited to, power consumption, maximum torque output,cruising torque output, maximum attitude, cruising attitude, maximumvelocity, cruising velocity, and the like thereof. For example andwithout limitation, expected actuator performance model 124 mayhighlight individual performance parameters of each actuator based on asensor disposed on each actuator. In a non-limiting embodiment, expectedactuator performance model 124 can be used to assess the performance ofthe aircraft actuators by comparing expected actuator performance model124 to actuator performance model 120 and analyzing the differencebetween the data from the two models. In a non-limiting embodiment,controller 112 may feed flight simulator 116 the ideal and/or peakperformance parameters of an aircraft and its actuators to simulateexpected actuator performance model 120 based on those ideal and/or peakperformance parameters. In a non-limiting embodiment, expected actuatorperformance model 120 may include a plurality of expected actuatorperformance model 124 depicting a different failure modes of an aircraftand/or an aircraft's actuators. For example and without limitation, arotor may fail by outputting max thrust, outputting zero thrust, or bestuck at an intermediate setting. In some embodiments, models aredetermined based on and/or for various actuator settings. In variousembodiments, only highly likely or relatively dangerous actuator failuremodes are considered and modeled. For example, a rotor may be modeledfor a zero-output case but not for a pinned high case. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of the various models and comparisons consistent with thisdisclosure.

With continued reference to FIG. 1, flight simulator 116 may beconfigured to simulate a virtual representation. The virtualrepresentation may represent a virtualization of actuator performancemodel 124 and/or expected actuator performance model 120. A “virtualrepresentation” includes any model or simulation accessible by computingdevice which is representative of a physical phenomenon, for examplewithout limitation at least an actuator. In some cases, virtualrepresentation may be interactive with flight simulator 116. Forexample, in some cases, data may originate from virtual representationand be input into flight simulator 116. Alternatively or additionally,in some cases, the virtual representation may modify or transform dataalready available to flight simulator 116. The virtual representationmay include an electric aircraft and/or one or more actuator of theelectric aircraft. In some cases, at least electric aircraft may includean electric vertical take-off and landing (eVTOL) aircraft, for examplea functional flight-worthy eVTOL aircraft. In some cases, at least avirtual representation may include a virtual controller area network.Virtual controller area network may include any virtual controller areanetwork. A controller area network may include a plurality of physicalcontroller area network buses communicatively connected to the aircraft,such as an electronic vertical take-off and landing (eVTOL) aircraft asdescribed in further detail below. A physical controller area networkbus may be vehicle bus unit including a central processing unit (CPU), aCAN controller, and a transceiver designed to allow devices tocommunicate with each other's applications without the need of a hostcomputer which is located physically at the aircraft. Physicalcontroller area network (CAN) bus unit may include physical circuitelements that may use, for instance and without limitation, twistedpair, digital circuit elements/FGPA, microcontroller, or the like toperform, without limitation, processing and/or signal transmissionprocesses and/or tasks; circuit elements may be used to implement CANbus components and/or constituent parts as described in further detailbelow. Physical CAN bus unit may include multiplex electrical wiring fortransmission of multiplexed signaling. Physical CAN bus unit may includemessage-based protocol(s), wherein the invoking program sends a messageto a process and relies on that process and its supportinginfrastructure to then select and run appropriate programing. Aplurality of physical CAN bus units located physically at the aircraftmay include mechanical connection to the aircraft, wherein the hardwareof the physical CAN bus unit is integrated within the infrastructure ofthe aircraft. Physical CAN bus units may be communicatively connected tothe aircraft and/or with a plurality of devices outside of the aircraft.

With continued reference to FIG. 1, controller 112 is configured toidentify defunct actuator 128 of the electric aircraft as a function ofthe sensor datum and the actuator performance model. A “defunctactuator,” for the purpose of this disclosure, is a malfunctioning orfailing actuator of an electric aircraft. In a non-limiting embodiment,defunct actuator 128 may include any actuator that may produce abnormaloutputs. For example and without limitation, defunct actuator 128 mayoutput a torque of 1.5 Newton-metre (Nm) while the remaining actuatorsmay output a torque of 3.6 Nm. In a non-limiting embodiment, actuatorsmay include different failure modes which are represented by variousexpected actuator performance model 120. For example, a rotor may failby outputting max thrust, outputting zero thrust, or be stuck at anintermediate setting. In some embodiments, models are determined basedon and/or for various actuator settings. In various embodiments, onlyhighly likely or relatively dangerous actuator failure modes areconsidered and modeled. For example, a rotor may be modeled for azero-output case but not for a pinned high case.

In a non-limiting embodiment, controller 112 may compare actuatorperformance model 124 with expected actuator performance model 120 toidentify defunct actuator 128. For example and without limitation,controller 112 may sort models based on their expected metrics andselect the model that has expected metrics that closely match actual orobserved metrics. In one example, the metrics compared include attitudeand rates of change in attitude of the aircraft. In some embodiments,controller 112 may compares observed metrics of the aircraft to expectedmetrics of the aircraft in an operable mode (e.g. no actuator failuresassociated with a “no failure” model). In a non-limiting embodiment,controller 112 may receive from flight simulator 116, a plurality ofexpected actuator performance model 120 which may include a model forevery possible failure mode. A “failure mode,” for the purpose of thisdisclosure, is any state of the electric aircraft in which one or moreactuators are defunct, malfunctioning, or failing. For example andwithout limitation, in an aircraft comprising four rotors, a model isdetermined for a first rotor failure, a second rotor failure, a thirdrotor failure, a fourth rotor failure, a first and second rotor failure,a first and third rotor failure, a first and fourth rotor failure, asecond and third rotor failure, and a second and fourth rotor failure.Multiple additional models may be determined including a model for norotor failure and all rotor failure in addition to a first, second, andthird rotor failure and a second, third, and fourth rotor failure. Insome embodiments, the number of models determined is equal to the numberof actuators squared plus one. In some embodiments, the number ofconsidered failure modes is less than the total possible failure modes.For example, models may not be determined for less likely failure modesin order to limit computations performed. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousembodiments of failure modes used to identify a defunct actuator 128consistent with this disclosure. In a non-limiting embodiment, theplurality of expected actuator performance model 120 may be sorted basedon similarity to an observed flight datum. For example, an expectedflight datum including expected attitude and expected attitude rategiven a failure mode is determined or otherwise generated based onand/or for each model. In some embodiments, the models are sorted basedon how closely their corresponding expected attitude and expectedattitude rate matches the observed attitude and attitude rate. Thesorted models may result in a sorted list of failure modes from mostlikely to least likely. For example, in the event a first rotor failuremode has a corresponding expected attitude that is 0.2 off from theobserved attitude and an expected attitude rate that is 0.1 off from theobserved attitude rate whereas a second rotor failure mode has acorresponding expected attitude that is 0.7 off from the observedattitude and an expected attitude rate that is 0.9 off from the observedattitude, the first rotor failure is determined to be more likely thanthe second rotor failure mode. In some embodiments, the summation of thedifference between expected attitude and observed attitude and thedifference between expected attitude rate and observed attitude rate isused to sort the models. In some embodiments, expected attitude isweighted more than expected attitude rate or vice versa. A model'ssimilarity to observed metrics may be determined using variouscalculations based on the expected and observed values in variousembodiments. In a non-limiting embodiment, controller 112 may identifydefunct actuator 128 or one or more defunct actuators 128 based on thesorted model. For example, the failure mode top of the sorted list maybe selected. Actuator failures that correspond to the failure mode aredetermined to be in effect.

With continued reference to FIG. 1, controller 112 is configured togenerate actuator allocation command datum 152 as a function of at leastthe actuator performance model 120 and at least the identification ofdefunct actuator 128. An “actuator allocation command datum,” for thepurpose of this disclosure, is command for a torque allocation to beapplied to one or more actuators of the electric aircraft. In anon-limiting embodiment, actuator allocation command datum 152 mayinclude unique torque allocations for each actuator. For example andwithout limitation, actuator allocation command datum 152 may instructeach functioning actuator to allocate a torque output of 4 Nm andinstruct defunct actuator 128 to allocate a torque output of 0.4 Nm. Forexample and without limitation, actuator allocation command datum 152may instruct one or more defunct actuators 128 to command a torque of 0Nm and the remaining functioning actuators a torque of 6 Nm. In anon-limiting embodiment, actuator allocation command datum 152 may begenerated as a function of a torque allocation. For instance and withoutlimitation, torque allocation may be consistent with the description oftorque allocation in U.S. patent application Ser. No. 17/197,427 filedon Mar. 10, 2021 and titled, “SYSTEM AND METHOD FOR FLIGHT CONTROL INELECTRIC AIRCRAFT”, which is incorporated herein in its entirety byreference. In a non-limiting embodiment, controller 112 may generateactuator allocation command datum 152 as a function of amachine-learning model. In a non-limiting embodiment, machine-learningmodel may generate actuator allocation command datum 152 given sensordatum 108, actuator performance model 124, and/or identification ofdefunct actuator 128 and data describing it as inputs. In a non-limitingembodiment, machine-learning model may generate actuator allocationcommand datum 152 using moment datum 144 as an input. In a non-limitingembodiment, controller 112 may receive training data correlating sensordatum 108 to actuator performance model 124 which may include a failuremode or a model identifying defunct actuator 128.

With continued reference to FIG. 1, controller 112 may comprises outerloop controller 132, wherein outer loop controller 132 may be configuredto generate rate setpoint 136 as a function of sensor datum 108 and theidentification of defunct actuator 128. Outer loop controller 132 mayinclude one or more computing devices consistent with this disclosureand/or one or more components and/or modules thereof. For instance andwithout limitation, outer loop controller may be consistent with outerloop controller in U.S. patent application Ser. No. 17/218,428 andtitled “METHODS AND SYSTEMS FOR FLIGHT CONTROL CONFIGURED FOR USE IN ANELECTRIC AIRCRAFT,” which is incorporated herein by reference in itsentirety. Outer loop controller 132 may be implemented using amicrocontroller, a hardware circuit such as an FPGA, system on a chip,and/or application specific integrated circuit (ASIC). Outer loopcontroller 132 may be implemented using one or more analog elements suchas operational amplifier circuits, including operational amplifierintegrators and/or differentiators. Outer loop controller 132 may beimplemented using any combination of the herein described elements orany other combination of elements suitable therefor. Outer loopcontroller 132 may be configured to input one or more parameters, suchas input datum 108 and/or at least an aircraft angle 116 and output ratesetpoint 136. Outer loop controller 132 may periodically detect one ormore errors between aircraft angles and commanded angles in any one ofpitch, roll, yaw, or a combination thereof. For example, and withoutlimitation, outer loop controller 132 may detect the error between thecommanded and detected aircraft angle and command one or more propulsorsand or flight components consistent with the entirety of this disclosureto reduce said error in one or more iterations. Outer loop controller132 may be closed by a PI controller with integral anti-windup viaback-calculation. Additional logic is present to prevent integral windupwhile grounded on a not perfectly level surface. Gains may be reduced atlarge amplitude in order to reduce overshoot on large inputs. Thisexcessive overshoot may be due in part to linear systems having constantpercent overshoot, so at larger amplitudes, the absolute value of theovershoot becomes (potentially unacceptably) large. Additionally, onlarge step inputs, motor saturation (a nonlinear effect) may occur forextended periods of time and causes overshoot to increase. In extremecases, the occurrence of motor saturation without any gain reduction maylead to unrecoverable tumbles. This gain reduction may be implemented asa (soft) rate command limit. In particular, this reduction may be givenby the piecewise combination of a linear function and the square rootfunction. Note that the input/output relationship may be monotonicallyincreasing, so increased angle error or integral action always makes itthrough to the inner loop, even if the gain reduction may be engaged.For inputs less than the knee, set to 20 deg/s, the input may be notchanged. Above the knee, the output may be given bysign(input)*sqrt(abs(input)*knee). The effective gain at any point tothe right of the knee may be then given by sqrt(abs(input)*knee)/input.This gain decrease at large amplitudes has been shown in simulation tostabilize the vehicle when subject to inputs that would otherwisedestabilize the vehicle into an unrecoverable tumble. For the vastmajority of maneuvers, this soft rate limit may be set high enough tonot be noticeable.

Outer loop controller 132 may include circuitry, components, processors,transceivers, or a combination thereof configured to receive and/or sendelectrical signals. Outer loop controller 132 may include aproportional-integral-derivative (PID) controller. PID controllers mayautomatically apply accurate and responsive correction to a controlfunction in a loop, such that over time the correction remainsresponsive to the previous output and actively controls an output.Controller 112 may include damping, including critical damping to attainthe desired setpoint, which may be an output to a propulsor in a timelyand accurate way. Outer loop controller 132 may include components,circuitry, receivers, transceivers, or a combination thereof. Outer loopcontroller 132 may be configured to generate rate setpoint 136 as afunction of sensor datum 108 and identification of defunct actuator 128.In a non-limiting embodiment, controller 112 may use an outer angle loopdriving an inner rate loop to provide closed loop control with setpointsof desired pitch attitude, roll attitude, and yaw rate provided directlyby the pilot. The outer (angle) loop provides rate setpoint 136. Ratesetpoint 136 may include the desired rate of change of one or moreangles describing the aircraft's orientation, heading, and propulsion,or a combination thereof. Rate setpoint 136 may include the pilot'sdesired rate of change of aircraft pitch angle, consistent with pitchangles, and largely at least an aircraft angle 116 in the entirety ofthis disclosure. Rate setpoint 136 may include a measurement in aplurality of measurement systems including quaternions or any othermeasurement system as described herein.

With continued reference to FIG. 1, controller 112 may comprise innerloop controller 140, wherein inner loop controller 140 may be configuredto generate moment datum 144 as a function of rate setpoint 136. Momentdatum 144 may include any information describing the moment of anaircraft. Moment datum 144 includes information regarding pilot's desireto apply a certain moment or collection of moments on one or moreportions of an electric aircraft, including the entirety of theaircraft. For instance and without limitation, inner loop controller maybe consistent with inner loop controller in U.S. patent application Ser.No. 17/218,428 and titled “METHODS AND SYSTEMS FOR FLIGHT CONTROLCONFIGURED FOR USE IN AN ELECTRIC AIRCRAFT,” which is incorporatedherein by reference in its entirety. Inner loop controller 140 may beimplemented in any manner suitable for implementation of outer loopcontroller. The inner loop of the controller may be composed of alead-lag filter for roll rate, pitch rate, and yaw rate, and anintegrator that acts only on yaw rate. Integrators may be avoided on theroll and pitch rate because they introduce additional phase lag that,coupled with the phase lag inherent to slow lift fans or another type ofone or more propulsors, limits performance. Furthermore, it may not benecessary to have good steady state error in roll and pitch rate, whichan integrator helps achieve in yaw rate. A final component of the innerloop may include gain scheduling on lift lever input. As previouslydiscussed, the only controller change between low speed flight and fullywing-borne flight may be this gain scheduling. The plot below shows theinput to output gain of this function for varying lift lever inputs. Atanything above the assisted lift input corresponding to zero airspeedflight, the full requested moment from the inner loop may be sent to themixer. At assisted lift levels lower than this, the requested momentfrom the inner loop may be multiplied by a gain that linearly decays tozero as shown in the plot below. The exact shape of this gain reductionmay be open to change slightly. Experimentation in simulation has shownthat anything between a square root function up to the IGE averagetorque setting and the linear map shown above works acceptably. Becausethe moment that can be generated by the control surfaces in pitch may besuch a strong function of angle of attack, the relatively smalldifference in hover moment achieved between the linear and square rootmaps may be washed out by the angle of attack variation in a transition.At low lift lever input, the plane would have to have significantunpowered lift (and therefore airspeed) to not lose altitude. In thiscase, the control surface effectivity will be significant, and fullmoment production from the lift motors will not be necessary. When thelift lever may be all the way down, the lift motors may stop rotationand stow into a low drag orientation. Then, the only control authoritycomes from the aerodynamic control surfaces, and the plane controlledexclusively via manual pilot inputs. On transition out from vertical tocruise flight, the coordination and scheduling of control may beintuitive and straightforward. In a non-limiting example, during thetransition in, or decelerating from an aborted takeoff, it may beimportant that the pilot not decrease assisted lift below a 15% averagetorque threshold in order to maintain aircraft control and not developan unrecoverable sink rate when operating in certain airspeed regimessuch as the transition regime. A mechanical detent may be installed inthe lift lever, throttle, or any control input, to provideproprioceptive feedback when crossing this threshold which should occuroperationally only during the terminal phases of a vertical landing.

With continued reference to FIG. 1, inner loop controller 140 mayinclude a lead-lag-filter. Inner loop controller 140 may include anintegrator. The attitude controller gains are scheduled such that fullgain authority may be only achieved when the assisted lift lever may begreater than 50% torque, which corresponds to a nominal torque requiredto support the aircraft without fully developed lift from the wing. Ataverage torque levels lower than said nominal levitation torque, theoutput of the inner loop (desired moment vector to apply to the vehicle)may be directly scaled down. This decrease in moment generated at thelift rotors may be designed to be directly complementary to the increasein aerodynamic control surface effectivity as the dynamic pressurebuilds on the flying wing and the flying surfaces. As a result, thetotal moment applied to the vehicle for a given pilot input may be keptnear constant.

With continued reference to FIG. 1, controller 112 may comprise mixer148, wherein mixer 148 may be configured to generate actuator allocationcommand datum 152 as a function of moment datum 144. In a non-limitingembodiment, moment datum 144 may include a plurality of attitudecommands and allocates one or more outgoing signals, such as modifiedattitude commands and output torque command, or the like, to at least apropulsor, flight component, or one or more computing devices connectedthereto. For instance and without limitation, mixer may be consistentwith mixer in U.S. patent application Ser. No. 17/218,428 and titled“METHODS AND SYSTEMS FOR FLIGHT CONTROL CONFIGURED FOR USE IN ANELECTRIC AIRCRAFT,” which is incorporated herein by reference in itsentirety. Additionally and alternatively, mixer 148, as used herein, maybe described as performing “control allocation” or “torque allocation”.For example, mixer may take in commands to alter aircraft trajectorythat requires a change in pitch and yaw. Mixer may allocate torque to atleast one propulsor (or more) that do not independently alter pitch andyaw in combination to accomplish the command to change pitch and yaw.More than one propulsor may be required to adjust torques to accomplishthe command to change pitch and yaw, mixer would take in the command andallocate those torques to the appropriate propulsors consistent with theentirety of this disclosure. One of ordinary skill in the art, afterreading the entirety of this disclosure, will appreciate the limitlesscombination of propulsors, flight components, control surfaces, orcombinations thereof that could be used in tandem to generate someamount of authority in pitch, roll, yaw, and lift of an electricaircraft consistent with this disclosure.

With continued reference to FIG. 1, mixer 148 may be configured to solveat least an optimization problem, which may be an objective function. An“objective function,” as used in this disclosure, is a mathematicalfunction with a solution set including a plurality of data elements tobe compared. Mixer 148 may compute a score, metric, ranking, or thelike, associated with each performance prognoses and candidate transferapparatus and select objectives to minimize and/or maximize thescore/rank, depending on whether an optimal result may be represented,respectively, by a minimal and/or maximal score; an objective functionmay be used by mixer to score each possible pairing. At least anoptimization problem may be based on one or more objectives, asdescribed below. Mixer 148 may pair a candidate transfer apparatus, witha given combination of performance prognoses, that optimizes theobjective function. In various embodiments solving at least anoptimization problem may be based on a combination of one or morefactors. Each factor may be assigned a score based on predeterminedvariables. In some embodiments, the assigned scores may be weighted orunweighted. Solving at least an optimization problem may includeperforming a greedy algorithm process, where optimization may beperformed by minimizing and/or maximizing an output of objectivefunction. A “greedy algorithm” is defined as an algorithm that selectslocally optimal choices, which may or may not generate a globallyoptimal solution. For instance, mixer may select objectives so thatscores associated therewith are the best score for each goal. Forinstance, in non-limiting illustrative example, optimization maydetermine the pitch moment associated with an output of at least apropulsor based on an input.

Still referring to FIG. 1, at least an optimization problem may beformulated as a linear objective function, which mixer may optimizeusing a linear program such as without limitation a mixed-integerprogram. A “linear program,” as used in this disclosure, is a programthat optimizes a linear objective function, given at least a constraint;a linear program maybe referred to without limitation as a “linearoptimization” process and/or algorithm. For instance, in non-limitingillustrative examples, a given constraint might be torque limit, and alinear program may use a linear objective function to calculate maximumoutput based on the limit. In various embodiments, mixer may determine aset of instructions towards achieving a user's goal that maximizes atotal score subject to a constraint that there are other competingobjectives. A mathematical solver may be implemented to solve for theset of instructions that maximizes scores; mathematical solver may beimplemented on mixer and/or another device in flight control system 100,and/or may be implemented on third-party solver. At least anoptimization problem may be formulated as nonlinear least squaresoptimization process. A “nonlinear least squares optimization process,”for the purposes of this disclosure, is a form of least squares analysisused to fit a set of m observations with a model that is non-linear in nunknown parameters, where m is greater than or equal to n. The basis ofthe method is to approximate the model by a linear one and to refine theparameters by successive iterations. A nonlinear least squaresoptimization process may output a fit of signals to at least apropulsor. Solving at least an optimization problem may includeminimizing a loss function, where a “loss function” is an expression anoutput of which a ranking process minimizes to generate an optimalresult. As a non-limiting example, mixer may assign variables relatingto a set of parameters, which may correspond to score components asdescribed above, calculate an output of mathematical expression usingthe variables, and select an objective that produces an output havingthe lowest size, according to a given definition of “size,” of the setof outputs representing each of plurality of candidate ingredientcombinations; size may, for instance, included absolute value, numericalsize, or the like. Selection of different loss functions may result inidentification of different potential pairings as generating minimaloutputs.

With continued reference to FIG. 1, mixer 148 may be configured togenerate actuator allocation command datum 152 as a function of thetorque allocation. Actuator allocation command datum 152 may include atleast a torque vector. Actuator allocation command datum 152 may berepresented in any suitable form, which may include, without limitation,vectors, matrices, coefficients, scores, ranks, or other numericalcomparators, and the like. A “vector” as defined in this disclosure is adata structure that represents one or more quantitative values and/ormeasures of forces, torques, signals, commands, or any other datastructure as described in the entirety of this disclosure. A vector maybe represented as an n-tuple of values, where n is at least two values,as described in further detail below; a vector may alternatively oradditionally be represented as an element of a vector space, defined asa set of mathematical objects that can be added together under anoperation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and may be distributive with respect tofield addition. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute/as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes. One of ordinary skill in the art would appreciate avector to be a mathematical value consisting of a direction andmagnitude. A “torque”, for the purposes of this disclosure, refers to atwisting force that tends to cause rotation. Torque is the rotationalequivalent of linear force. In three dimensions, the torque may be apseudovector; for point particles, it may be given by the cross productof the position vector (distance vector) and the force vector. Themagnitude of torque of a rigid body depends on three quantities: theforce applied, the lever arm vector connecting the point about which thetorque may be being measured to the point of force application, and theangle between the force and lever arm vectors. A force appliedperpendicularly to a lever multiplied by its distance from the lever'sfulcrum (the length of the lever arm) may be its torque. A force ofthree newtons applied two meters from the fulcrum, for example, exertsthe same torque as a force of one newton applied six meters from thefulcrum. The direction of the torque can be determined by using theright-hand grip rule: if the fingers of the right hand are curled fromthe direction of the lever arm to the direction of the force, then thethumb points in the direction of the torque. One of ordinary skill inthe art would appreciate that torque may be represented as a vector,consistent with this disclosure, and therefore includes a magnitude offorce and a direction. “Torque” and “moment” are equivalents for thepurposes of this disclosure. Any torque command or signal herein mayinclude at least the steady state torque to achieve the initial vehicletorque signal 108 output to at least a propulsor.

With continued reference to FIG. 1, as previously disclosed, solving atleast an optimization problem may include solving sequential problemsrelating to vehicle-level inputs to at least a propulsor, namely pitch,roll, yaw, and collective force. Mixer 148 may solve at least anoptimization problem in a specific order. According to exemplaryembodiments, mixer 148 may solve at least an optimization problemwherein at least an optimization problem includes a pitch momentfunction. Solving may be performed using a nonlinear program and/or alinear program. Mixer may solve at least an optimization problem whereinsolving at least an optimization program may include solving a rollmoment function utilizing a nonlinear program to yield the desiredamount of roll moment as a function of the desired amount of pitchmoment. Mixer 148 may solve at least an optimization problem whereinsolving at least an optimization program may include solving acollective force function utilizing a nonlinear program to yield thedesired amount of collective force as a function of the desired amountof pitch moment and the desired amount of roll moment. Mixer 148 maysolve at least an optimization problem wherein solving at least anoptimization program may include solving a yaw moment function utilizinga nonlinear program to yield the desired amount of yaw moment, as afunction of the desired amount of pitch moment, the desired amount ofroll moment, and the desired amount of collective force. One of ordinaryskill in the art, after reading the entirety of this disclosure, willappreciate that any force program may be implemented as a linear ornon-linear program, as any linear program may be expressed as anonlinear program.

With continued reference to FIG. 1, mixer 148 may include one or morecomputing devices as described herein. Mixer 148 may be a separatecomponent or grouping of components from those described herein. Mixer148 is configured to generate actuator allocation command datum 152 as afunction of the torque allocation. Mixer 148 may be configured toallocate a portion of total possible torque amongst one or morepropulsors based on relative priority of a plurality attitude controlcommands and desired aircraft maneuver. In a non-limiting illustrativeexample, torque allocation between two attitude control components(e.g., pitch and roll or roll and yaw) may be based on the relativepriorities of those two attitude control components. Priority refers tohow important to the safety of the aircraft and any users whileperforming the attitude control component may be relative to the otherattitude control commands. Priority may also refer to the relativeimportance of each attitude control component to accomplish one or moredesired aircraft maneuvers. For example, pitch attitude controlcomponent may be the highest priority, followed by roll, lift, and yawattitude control components. In another example, the relative priorityof the attitude components may be specific to an environment, aircraftmaneuver, mission type, aircraft configuration, or other factors, toname a few. Torque allocator may set the highest priority attitudecontrol component torque allocation as close as possible given thetorque limits as described in this disclosure to the original commandfor the higher-priority attitude control component, in the illustrativeexample, pitch, then project to the value possible for the lowerpriority attitude control component, in this case, lift. The higherpriority attitude control component in the first torque allocation maybe the attitude control component with the highest overall priority.This process may be then repeated with lower priority attitude controlcomponent from the above comparison and the next highest down thepriority list. In a non-limiting illustrative example, the nexttwo-dimensional torque allocation problem solved would include lift androll attitude control commands. In embodiments, the lower priorityattitude command component has already been set form the previoustwo-dimensional torque allocation, so this may be projecting the closestpossible value for the third-level attitude command (roll in thisexample). This process would repeat again for the third and fourthattitude components, in this non-limiting example, roll and yaw attitudecontrol components. Since roll may be prioritized over yaw, the rollattitude control command would be preserved, and yaw would be sacrificedas a function of the vehicle torque limits as described herein. Afterthe sequence of two-dimensional attitude control component torqueallocation are completed and four prioritized attitude componentcommands are set, one or more components may send out commands to flightcontrol surfaces/propulsors to generate the set torque values allocatedin the foregoing process. As a non-limiting example of one step in thetorque allocation process, pitch axis may represent the command orplurality of attitude commands inputted to mixer 148 as describedherein, such as moment datum 140. Pitch axis may be conditioned oraltered to be inputted to mixer 148. For example, and withoutlimitation, initial vehicle torque signal may include pitch and liftcommands within plurality of attitude commands. Mixer 148 may alsoreceive at least a moment datum 140, which may be represented withoutlimitation by a box plotted within the pitch and lift axes. A pointwhere pitch command and lift command intersect may represent initialvehicle torque signal as projected onto exemplary graph of pitch andlift axes, which may be the same or similar to initial vehicle torquesignal as disclosed in the entirety of this disclosure. Mixer 148utilizes prioritization data as described in the entirety of thisdisclosure to solve this two-dimensional problem by preserving thehigher priority command and sacrificing the lower priority command. Thisprioritization preservation process may be illustrated, as anon-limiting example by placement of a modified attitude command,wherein the pitch command was preserved (horizontally translated andtherefore unchanged from the initial command), while the lift commandwas lessened to bring the modified attitude command within vehicletorque limits (the box). The modified attitude command, as discussed inthe entirety of this disclosure, may be further combined, modified,conditioned, or otherwise adjusted to produce output torque command tothe plurality of propulsors. The remaining vehicle torque represents theremaining torque capability in one or more propulsors before, during,and after an aircraft maneuver. The remaining vehicle torque may includean individual propulsor's remaining torque capability, one or more ofpitch, roll, yaw, and lift, capabilities of one or more propulsors, theremaining vehicle-level torque or power for subsequent maneuvers. Theremaining vehicle torque may be displayed to a pilot or user. Theabove-described may be a non-limiting example of one step in the torqueallocation process. Torque allocation process may be similar or the sameprocess as described above with the torque limits adjusted for inertiacompensation. Mixer 148 may be disposed fully or partially within mixerany mixer as disclosed herein. Mixer 148 may include one or morecomputing devices as described herein. Mixer 148 also receives at leasta vehicle torque limit represented by an imaginary box plotted withinthe pitch and lift axes, which may be the same as, or similar to atleast a vehicle torque limit. Here instead of the box being made ofstraight linear sides, the inertia compensation as previously discussedcreates curved limits, wherein certain plurality of attitude commandsmay be allowed whereas without inertia compensation they would beoutside of the limits. Where the pitch command and lift commandintersect may be the initial vehicle torque signal, which may be thesame or similar to initial vehicle torque signal as disclosed in theentirety of this disclosure. Mixer 148 utilizes prioritization data asdescribed in the entirety of this disclosure to solve thistwo-dimensional problem by preserving the higher priority command andsacrificing the lower priority command. This prioritization preservationprocess may be shown by the placement of modified attitude command,wherein the pitch command was preserved (horizontally translated andtherefore unchanged from the initial command), while the lift commandwas lessened to bring the modified attitude command within vehicletorque limits (the box). Actuator allocation command datum 152effectively commands the amount of torque to one or more propulsors toaccomplish the closest vehicle level torque to initial vehicle torquesignal as possible given certain limits, maneuvers, and aircraftconditions. Modified attitude command, as discussed in the entirety ofthis disclosure, may be further combined, modified, conditioned, orotherwise adjusted to produce output torque command to the plurality ofpropulsors. The remaining vehicle torque represents the remaining torquecapability in one or more propulsors before, during, and after anaircraft maneuver. The remaining vehicle torque may include anindividual propulsor's remaining torque capability, one or more ofpitch, roll, yaw, and lift, capabilities of one or more propulsors, theremaining vehicle-level torque or power for subsequent maneuvers.

Referring now to FIG. 2, an exemplary embodiment of outer loopcontroller 200 is presented in block diagram form. Outer loop controller200 may be consistent with any outer loop controller as describedherein. Outer loop controller 200 may include attitude error 204.Attitude error 204 may include a measurement of the difference betweenthe commanded at least an aircraft angle 116 and the actual angle of theaircraft in any of pitch, roll, yaw, or a combination thereof. Theattitude error 204 may include a percentage, measurement in degrees,measurement in radians, or one or more representations of a differencein commanded aircraft angle as a function of input datum 104 and actualangle of aircraft in the aforementioned attitudes. Attitude error 204may include measurements as detected by one or more sensors configuredto measure aircraft angle like an IMU, gyroscope, motion sensor, opticalsensor, a combination thereof, or another sensor of combination ofsensors. Outer loop controller 200 may include clipped moment 208 as aninput to controller. Clipped moment 208 may include one or more elementsof data that have been selected from a larger sample size or range.Clipped moment 208 may have been selected for its lack of noise,improved efficiency, or accuracy of moment associated with any one ormore elements of an electric aircraft consistent with the entirety ofthis disclosure. Gain may be a linear operation. Gain compression may benot linear and, as such, its effect may be one of distortion, due to thenonlinearity of the transfer characteristic which also causes a loss of‘slope’ or ‘differential’ gain. So, the output may be less than expectedusing the small signal gain of the amplifier. In clipping, the signalmay be abruptly limited to a certain amplitude and may be therebydistorted in keeping under that level. This creates extra harmonics thatare not present in the original signal. “Soft” clipping or limitingmeans there isn't a sharp “knee point” in the transfer characteristic. Asine wave that has been softly clipped will become more like a squarewave with more rounded edges, but will still have many extra harmonics.Outer loop controller 200 may include Kp operational amplifier 212. Kpop amp 212 may include one or more constants configured to scale any oneor more signals in any control loop or otherwise computing devices foruse in controlling aspects of an electric aircraft. Outer loopcontroller 200 may include integral decoy logic 216. Outer loopcontroller 200 may include integrator 220. Integrator 220 may include anoperational amplifier configured to perform a mathematical operation ofintegration of a signal; output voltage may be proportional to inputvoltage integrated over time. An input current may be offset by anegative feedback current flowing in the capacitor, which may begenerated by an increase in output voltage of the amplifier. The outputvoltage may be therefore dependent on the value of input current it hasto offset and the inverse of the value of the feedback capacitor. Thegreater the capacitor value, the less output voltage has to be generatedto produce a particular feedback current flow. The input impedance ofthe circuit may be almost zero because of the Miller effect. Hence allthe stray capacitances (the cable capacitance, the amplifier inputcapacitance, etc.) are virtually grounded and they have no influence onthe output signal. Operational amplifier as used in integrator may beused as part of a positive or negative feedback amplifier or as an adderor subtractor type circuit using just pure resistances in both the inputand the feedback loop. As its name implies, the Op-amp Integrator is anoperational amplifier circuit that causes the output to respond tochanges in the input voltage over time as the op-amp produces an outputvoltage which may be proportional to the integral of the input voltage.In other words, the magnitude of the output signal may be determined bythe length of time a voltage may be present at its input as the currentthrough the feedback loop charges or discharges the capacitor as therequired negative feedback occurs through the capacitor. Input voltagemay be Vin and represent the input signal to controller such as one ormore of sensor datum 108 and/or attitude error 204. Output voltage Voutmay represent output voltage such as one or more outputs like ratesetpoint 232. When a step voltage, Vin may be firstly applied to theinput of an integrating amplifier, the uncharged capacitor C has verylittle resistance and acts a bit like a short circuit allowing maximumcurrent to flow via the input resistor, Rin as potential differenceexists between the two plates. No current flows into the amplifiersinput and point X may be a virtual earth resulting in zero output. Asthe impedance of the capacitor at this point may be very low, the gainratio of X_(C)/R_(IN) may be also very small giving an overall voltagegain of less than one, (voltage follower circuit). As the feedbackcapacitor, C begins to charge up due to the influence of the inputvoltage, its impedance Xc slowly increase in proportion to its rate ofcharge. The capacitor charges up at a rate determined by the RC timeconstant, (τ) of the series RC network. Negative feedback forces theop-amp to produce an output voltage that maintains a virtual earth atthe op-amp's inverting input. Since the capacitor may be connectedbetween the op-amp's inverting input (which may be at virtual groundpotential) and the op-amp's output (which may be now negative), thepotential voltage, Vc developed across the capacitor slowly increasescausing the charging current to decrease as the impedance of thecapacitor increases. This results in the ratio of Xc/Rin increasingproducing a linearly increasing ramp output voltage that continues toincrease until the capacitor may be fully charged. At this point thecapacitor acts as an open circuit, blocking any more flow of DC current.The ratio of feedback capacitor to input resistor (X_(C)/R_(IN)) may benow infinite resulting in infinite gain. The result of this high gain(similar to the op-amps open-loop gain), may be that the output of theamplifier goes into saturation as shown below. (Saturation occurs whenthe output voltage of the amplifier swings heavily to one voltage supplyrail or the other with little or no control in between). The rate atwhich the output voltage increases (the rate of change) may bedetermined by the value of the resistor and the capacitor, “RC timeconstant”. By changing this RC time constant value, either by changingthe value of the Capacitor, C or the Resistor, R, the time in which ittakes the output voltage to reach saturation can also be changed forexample. Outer loop controller 200 may include a double integrator,consistent with the description of an integrator with the entirety ofthis disclosure. Single or double integrators consistent with theentirety of this disclosure may include analog or digital circuitcomponents. Outer loop controller 200 may include Ki operationalamplifier 224. Ki op amp 224 may be a unique constant configured toscale any one or more signals or data as described herein with referenceto kp op amp 212. Outer loop controller 200 may include large amplitudegain reduction 228. Large amplitude gain reduction 228 may be configuredto reduce gain on large amplitude input signals consistent with theabove description. Compression of gain may be caused by non-linearcharacteristics of the device when run at large amplitudes. With anysignal, as the input level may be increased beyond the linear range ofthe amplifier, gain compression will occur. A transistor's operatingpoint may move with temperature, so higher power output may lead tocompression due to collector dissipation. But it may be not a change ingain; it may be non-linear distortion. The output level stays relativelythe same as the input level goes higher. Once the non-linear portion ofthe transfer characteristic of any amplifier may be reached, anyincrease in input will not be matched by a proportional increase inoutput. Thus, there may be compression of gain. Also, at this timebecause the transfer function may be no longer linear, harmonicdistortion will result. In intentional compression (sometimes calledautomatic gain control or audio level compression as used in devicescalled ‘dynamic range compressors’, the overall gain of the circuit maybe actively changed in response to the level of the input over time, sothe transfer function remains linear over a short period of time. A sinewave into such a system will still look like a sine wave at the output,but the overall gain may be varied, depending on the level of that sinewave. Above a certain input level, the output sine wave will always bethe same amplitude. The output level of Intentional compression variesover time, in order to minimize non-linear behavior. With gaincompression, the opposite may be true, its output may be constant. Inthis respect intentional compression serves less of an artistic purpose.

Referring now to FIG. 3, an exemplary embodiment of inner loopcontroller 300 is presented in block diagram form. Inner loop controller300 may include clipped moment 308 as an input to controller. Gain maybe a linear operation. Gain compression may be not linear and, as such,its effect may be one of distortion, due to the nonlinearity of thetransfer characteristic which also causes a loss of ‘slope’ or‘differential’ gain. So, the output may be less than expected using thesmall signal gain of the amplifier. In clipping, the signal may beabruptly limited to a certain amplitude and may be thereby distorted inkeeping under that level. This creates extra harmonics that are notpresent in the original signal. “Soft” clipping or limiting means thereisn't a sharp “knee point” in the transfer characteristic. A sine wavethat has been softly clipped will become more like a square wave withmore rounded edges but will still have many extra harmonics. Inner loopcontroller 300 may include Kp operational amplifier 312. Inner loopcontroller 300 may include integral decoy logic 316. Inner loopcontroller 300 may include integrator 320. Integrator 320 may include anoperational amplifier configured to perform a mathematical operation ofintegration of a signal; output voltage may be proportional to inputvoltage integrated over time. An input current may be offset by anegative feedback current flowing in the capacitor, which may begenerated by an increase in output voltage of the amplifier. The outputvoltage may be therefore dependent on the value of input current it hasto offset and the inverse of the value of the feedback capacitor. Thegreater the capacitor value, the less output voltage has to be generatedto produce a particular feedback current flow. The input impedance ofthe circuit almost zero because of the Miller effect. Hence all thestray capacitances (the cable capacitance, the amplifier inputcapacitance, etc.) are virtually grounded and they have no influence onthe output signal. Operational amplifier as used in integrator may beused as part of a positive or negative feedback amplifier or as an adderor subtractor type circuit using just pure resistances in both the inputand the feedback loop. As its name implies, the Op-amp Integrator is anoperational amplifier circuit that causes the output to respond tochanges in the input voltage over time as the op-amp produces an outputvoltage which may be proportional to the integral of the input voltage.In other words, the magnitude of the output signal may be determined bythe length of time a voltage may be present at its input as the currentthrough the feedback loop charges or discharges the capacitor as therequired negative feedback occurs through the capacitor. Input voltagemay be Vin and represent the input signal to controller such as one ormore of sensor datum 108 and/or attitude error 304. Output voltage Voutmay represent output voltage such as one or more outputs like ratesetpoint 332. When a step voltage, yin may be firstly applied to theinput of an integrating amplifier, the uncharged capacitor C has verylittle resistance and acts a bit like a short circuit allowing maximumcurrent to flow via the input resistor, Rin as potential differenceexists between the two plates. No current flows into the amplifiersinput and point X may be a virtual earth resulting in zero output. Asthe impedance of the capacitor at this point may be very low, the gainratio of X_(C)/R_(IN) may be also very small giving an overall voltagegain of less than one, (voltage follower circuit). As the feedbackcapacitor, C begins to charge up due to the influence of the inputvoltage, its impedance Xc slowly increase in proportion to its rate ofcharge. The capacitor charges up at a rate determined by the RC timeconstant, (τ) of the series RC network. Negative feedback forces theop-amp to produce an output voltage that maintains a virtual earth atthe op-amp's inverting input. Since the capacitor may be connectedbetween the op-amp's inverting input (which may be at virtual groundpotential) and the op-amp's output (which may be now negative), thepotential voltage, Vc developed across the capacitor slowly increasescausing the charging current to decrease as the impedance of thecapacitor increases. This results in the ratio of Xc/Rin increasingproducing a linearly increasing ramp output voltage that continues toincrease until the capacitor may be fully charged. At this point thecapacitor acts as an open circuit, blocking any more flow of DC current.The ratio of feedback capacitor to input resistor (X_(C)/R_(IN)) may benow infinite resulting in infinite gain. The result of this high gain,similar to the op-amps open-loop gain, may be that the output of theamplifier goes into saturation as shown below. (Saturation occurs whenthe output voltage of the amplifier swings heavily to one voltage supplyrail or the other with little or no control in between). The rate atwhich the output voltage increases (the rate of change) may bedetermined by the value of the resistor and the capacitor, “RC timeconstant”. By changing this RC time constant value, either by changingthe value of the Capacitor, C or the Resistor, R, the time in which ittakes the output voltage to reach saturation can also be changed forexample. Inner loop controller 300 may include a double integrator,consistent with the description of an integrator with the entirety ofthis disclosure. Single or double integrators consistent with theentirety of this disclosure may include analog or digital circuitcomponents. Inner loop controller 300 may include Ki operationalamplifier 324. Inner loop controller 300 may include lead-lag filters328 consistent with the description of lead-lag filters herein below.Inner loop controller 300 may include lift lever input 332 as describedherein below. Inner loop controller 300 may include Schedule on liftlever 236 as described herein below.

Inner loop controller 300 may include pitch rate damping. Adding pitchrate damping with the elevators may be the least intrusive form ofaugmentation that has been suggested. In this scheme, the elevator inputmay be a sum of the pilot input (as in fully manual flight) and acomponent that arrests pitch rate as measured by the IMU's such as IMU112. The scheduling on the lift lever may be such that in forward flight(with 0 assisted lift), the full damping may be active. As the liftlever rises above some value (set to 0.1), the damping rolls off so thatvery low airspeed behavior may be handled entirely by the attitudecontroller. The higher this value may be set, the more active theelevator damping will be at low-speed flight (i.e., flight withsubstantial assisted lift). The saturation on the damping term ensuresthat the pilot has some amount of control authority regardless of whatthe augmentation attempts to do. With this design, as with the baselinedesign, there may be no blending between modes required duringacceleration from lift assisted flight to fully wing-borne flight.Additionally, there may be no control discontinuity as the lift fansturn off and stow.

With continued reference to FIG. 3, an alternative augmentation strategymay be to close a pitch rate loop with the control surfaces. If onechooses to use this, note that in order to avoid blending betweencontrol modes while accelerating from low-speed flight to wing-borneflight, the control system commanding the lift rotors must also be RCRH(as opposed to the nominal ACAH). An RCRH low airspeed controllerpotentially increases pilot workload substantially. Also note that thegains appropriate for this controller change substantially across anelectric aircraft's range of cruise airspeeds (as elevator effectivitychanges with dynamic pressure). Since the lift lever will be all the waydown during cruise, lift lever can no longer use this signal as a proxyfor airspeed. Since using airspeed as an input would introduce anadditional low reliability system, the system would be forced to selectconstant gains that produce a stable system at all reasonable airspeeds.The resulting system would have poor performance at low airspeeds. Itmay be possible to approximate airspeed in cruise from knowledge of thepusher performance and the operating speed and torque. Such an estimateof airspeed would likely be sufficient to enable the scheduling of gainson airspeed, which would result in less conservative design, and higherperformance. For the purposes of controlling a vehicle, controller 112are interested in the aerodynamic forces that the lift rotors canprovide. However, since the aerodynamic forces and torques that therotors generate are a function of speed, and the lift rotors havesubstantial inertia, simply passing the corresponding steady statetorque commands to the motor will result in a slow thrust response. Ifthis substantial phase lag may be not compensated for, performance willbe severely limited. Because controller 112 have a good understanding ofthe physics involved, controller 112 can apply a dynamic inverse of therotor model to the steady state torque signals in order to obtain betterspeed tracking, and therefore better thrust tracking. Intuitively, thisdynamic inverse adds a “kick” forward when the incoming signal increasessharply and adds a “kick” backwards when the incoming signal decreasessharply. Once the car may be at speed, one likely only needs one quarterthrottle to maintain speed, which suggests that holding one quarterthrottle for a sufficiently long time starting from a low speed wouldeventually accelerate the car to the desired speed. Of course, if oneuses full throttle to get up to speed, and then returns to quarterthrottle to hold speed, a faster response can be achieved. This may bethe core idea of what the dynamic inverse does. To apply a dynamicinverse, controller 112 first generate a model based on Euler's equationin 1 dimension. Here, I may be the fan inertia about the axis ofrotation, omega may be the angular velocity of the motor, \tau_{motor}may be the shaft torque generated by the motor, and \tau_{aero} may bethe aerodynamic shaft torque. Because the aerodynamic term may benonlinear in the speed state, controller 112 will omit this from thedynamic inversion for simplicity and handle it separately. Eventually,the torque command that controller 112 send to the motor will be a sumof a softened dynamic inverse of the motor inertia, and an approximationof the aerodynamic torque as below. First, controller 112 will determinethe value of the inertia dynamic inverse term. When controller 112inverts the inertia-only model (i.e. obtain the output→input responserather than the input→output response), controller 112 will end up witha pure derivative, which has an infinite high frequency response, andmay be thus not desirable. However, if controller 112 passed a desiredspeed through this transfer function (given below), the resulting torqueoutput would perfectly reproduce the desired speed. To make this work ona real system with torque limits, controller 112 will add a first orderlow pass filter in series with the dynamic inverse sI. If the motors hadunlimited torque capability, the resulting dynamics from input to motorspeed would be just the low pass dynamics. Note that a motor speedcommand may be present in this expression. However, controller 112 wouldlike to avoid closing a speed loop on the lift motors. The decision tonot close a speed loop was made on the belief that the thrust-torquerelationship was more constant than the thrust-speed relationship foredgewise flight. This may be not the case; both relationships varysimilarly with edgewise airspeed according to DUST simulations. Thisdecision may be re-evaluated in the future. However, because speed maybe the only state of the system, controller 112 may be forced togenerate some speed as input to this filter. Note that this speed doesnot have to be particularly accurate—there are no loops being closed onit, and this dynamic inverse decays to 0 quickly after the input signalstops changing. An appropriate means to generate this pseudo-referencespeed may be to use the well-known approximation for the staticspeed-torque relationship for a fan: Using this relationship, controller112 can compute the approximate steady state speed that corresponds to agiven torque input. Then, this speed signal may be passed through thedynamic inverse of the inertia only system. If this was the only torquethat was applied to the lift motors in a vacuum (i.e., no aero drag),the lift rotors would track speeds reasonably well. Of course, this maybe not the case, and controller 112 must still account for theaerodynamic torque. If controller 112 could always apply the exactaerodynamic torque experienced by the fan (but in the opposite sense)with the motor, any additional input would “see” only the inertia of thefan and motor. If this additional input may be the inertia-only dynamicinverse, then controller 112 would obtain the desired first order lowpass response in speed. Consider the following non-limiting example ofbootstrapping. If controller 112 assumes that controller 112 has a goodapproximation of aerodynamic torque and motor saturation does notengage, then the motor speed response (and therefore the aero torque,approximately) will be a first order low pass filter, with time constant\tau_{ff}. This tells us that controller 112 can approximate theaerodynamic torque by passing the steady state torque command through asimilar first order transfer function. The combination of this filteredsteady state torque and dynamic inversion of the approximatedcorresponding speed may be shown below. To implement this in discretetime, the transfer functions are discretized using the Tustin, orBilinear transform. Setting \tau_{ff} and \tau_{fwd} involves simulationof the system subject to different size and direction of input changesabout different operating points. These time constants are tweaked tomake the fans spin up as quickly as possible over a range of inputs.Intuitively, an excessively large time constant results in a slowresponse. However, a very short time constant also results in a slowresponse. With a very short time constant, the amplitude of the initialkick from the dynamic inverse may be very large, but also very short induration. As a result of motor saturation, the total achieved energyincrease from the kick may be low. An intermediate value of timeconstant (set to approximately 0.13) provides a faster response thaneither extreme. Due to the nature of the dynamic inverse, this systemamplifies noise in the steady state torque command. To avoid thisbecoming a nuisance while the aircraft may be grounded, the dynamicinverse term may be scheduled on the position of the lift lever in thesame way as the inner loop gains, but with a lower threshold. That maybe, for 0 lift lever input, there may be 0 dynamic inversioncontribution. This contribution ramps up linearly to full at 5% liftlever input. This inertia compensation (or something functionallysimilar), which may be essentially a lead-lag filter, but withphysically derived pole and zero locations, may be essential to thehigh-performance operation of any vehicle with slow control actuators.Without this, the phase lag introduced by the actuators makes itimpossible to achieve bandwidth sufficient for satisfactory handlingqualities. For well-flown transitions, the lift lever position may be agood proxy for airspeed, which directly determines the effectiveness ofthe conventional control surfaces. This follows from the fact that at afixed angle of attack, dynamic pressure on the wing and unpowered liftare linearly related. Therefore, in order to maintain altitude (which apilot would tend to do), one would need to lower the lift lever asairspeed increases. In the case that a pilot were to rapidly pull up onthe lift lever not in accordance with a decrease in airspeed, a pilot'scontrol inputs would produce more than nominal control moment on thevehicle due to lift fan gains not being scheduled down and high dynamicpressure. In simulation, this scenario has been shown to benon-catastrophic, although it will likely be somewhat violent as thevehicle accelerates upwards rapidly and experiences some attitudetransients. It may be easy to understand that each motor can only outputa torque between some lower limit and some upper limit. If controller112 draw the area that corresponds to these available motor commands forthe 2-fan system, controller 112 find that a “box” may be formed. Ifcontroller 112 assume a linear torque-thrust relationship, then so longas the motors do not rotate on the body, the map from this acceptablebox in the motor torque space to the acceptable box in the space wherethe axes are vehicle level upward thrust and torque may be linear.Therefore, the shape can only be scaled, flipped, and rotated, butstraight edges remain straight, and the number of vertices cannotchange. With this transformation done, controller 112 can now readilydetermine if a particular commanded force and torque combination may bepossible to achieve. Suppose that controller 112 chooses to prioritizevehicle level torque over force. In the case that the force and torquecombination may be inside the box, no saturation occurs—the mixer may beable to achieve the request, and no prioritization may be needed.Suppose instead that some points with the desired torque are within thebox, but none of these points have the desired force. Algorithmically,controller 112 first get the achieved torque to match the desired torqueas closely as possible. Then, that value may be locked down, and thensubject to that constraint, controller 112 matches the desired thrust asclosely as possible. In this case, the desired torque is achieved, butthe desired thrust is not. Mathematically, this is two sequentiallysolved linear programs (linear objective, linear constraints). Becausecontroller 112 knew the map from motor torques to vehicle torques, andbecause that map is invertible, controller 112 can now apply the inverseof this map to get a motor torque command 148 from the point controller112 identified in the vehicle torque space. Since the point is insidethe box in the vehicle torque space, it is guaranteed to also be insidethe box in the motor torque vector space, and thus guarantees that theresulting torque commands will be within the limits of the motors. Notethat controller 112 have not only resolved the motor saturation,controller 112 also know how much force and torque controller 112 aretrying to produce (i.e. Controller 112 haven't blindly done someclipping/rescaling of the motor signals). While this example uses onlytwo dimensions, the principle may be the same in higher dimensions. Thesolution method used may be slightly different than what may be shownhere, but the concept may be the same. Finally, it is important to notethat throughout this process, controller 112 has assumed that torquecorresponds to thrust. This may be only true in the case of steady stateoperation. Because the lift fans or one or other propulsors take asubstantial amount of time to spin up, this assumption may be notnecessarily accurate. As a result, the mixer's estimate of achievedmoment may be not accurate for rapidly changing inputs without inertiacompensation. Controller 112 can use a behavioral model of the lift fansor speed feedback to better approximate the true moment acting on theaircraft due to powered lift.

Referring now to FIG. 4, flow diagram of an exemplary method 400 forflight control for managing actuators for an electric aircraft isprovided. Method 400, at step 405, includes receiving, by a controller,a sensor datum from at least a sensor. Sensor datum may include anysensor datum as described herein. Controller may include any controlleras described herein. In a non-limiting embodiment, sensor datum mayinclude any data captured by any sensor as described in the entirety ofthis disclosure. Additionally and alternatively, sensor datum mayinclude any element or signal of data that represents an electricaircraft route and various environmental or outside parameters. In anon-limiting embodiment, sensor datum may include an element of thatrepresenting the safest, most efficient, shortest, or a combinationthereof, flight path. In a non-limiting embodiment, sensor datum mayinclude a degree of torque that may be sensed, without limitation, usingload sensors deployed at and/or around a propulsor and/or by measuringback electromotive force (back EMF) generated by a motor driving thepropulsor. In an embodiment, use of a plurality of independent sensorsmay result in redundancy configured to employ more than one sensor thatmeasures the same phenomenon, those sensors being of the same type, acombination of, or another type of sensor not disclosed, so that in theevent one sensor fails, the ability to detect phenomenon is maintainedand in a non-limiting example, a user alter aircraft usage pursuant tosensor readings. One of ordinary skill in the art will appreciate, afterreviewing the entirety of this disclosure, that motion may include aplurality of types including but not limited to: spinning, rotating,oscillating, gyrating, jumping, sliding, reciprocating, or the like.

With continued reference to FIG. 4, step 405 may include receiving aninput datum. At least pilot control may be communicatively connected toany other component presented in system, the communicative connectionmay include redundant connections configured to safeguard againstsingle-point failure. Pilot input may indicate a pilot's desire tochange the heading or trim of an electric aircraft. Pilot input mayindicate a pilot's desire to change an aircraft's pitch, roll, yaw, orthrottle. Aircraft trajectory is manipulated by one or more controlsurfaces and propulsors working alone or in tandem consistent with theentirety of this disclosure, hereinbelow. Pitch, roll, and yaw may beused to describe an aircraft's attitude and/or heading, as theycorrespond to three separate and distinct axes about which the aircraftmay rotate with an applied moment, torque, and/or other force applied toat least a portion of an aircraft. Sensor datum may include a flightdatum. In a non-limiting embodiment, flight datum may include aplurality of data describing the health status of an actuator of aplurality of actuators. In a non-limiting embodiment, the plurality ofdata may include a plurality of failure data for a plurality ofactuators. In a non-limiting embodiment, safety datum may include ameasured torque parameter that may include the remaining vehicle torqueof a flight component among a plurality of flight components. Remainingvehicle torque may include torque available at each of a plurality offlight components at any point during an aircraft's entire flightenvelope, such as before, during, or after a maneuver. For example, andwithout limitation, torque output may indicate torque a flight componentmust output to accomplish a maneuver; remaining vehicle torque may thenbe calculated based on one or more of flight component limits, vehicletorque limits, environmental limits, or a combination thereof. Vehicletorque limit may include one or more elements of data representingmaxima, minima, or other limits on vehicle torques, forces, attitudes,rates of change, or a combination thereof. Vehicle torque limit mayinclude individual limits on one or more flight components, structuralstress or strain, energy consumption limits, or a combination thereof.Remaining vehicle torque may be represented, as a non-limiting example,as a total torque available at an aircraft level, such as the remainingtorque available in any plane of motion or attitude component such aspitch torque, roll torque, yaw torque, and/or lift torque. In anon-limiting embodiment, controller 112 may mix, refine, adjust,redirect, combine, separate, or perform other types of signal operationsto translate pilot desired trajectory into aircraft maneuvers. In anonlimiting embodiment a pilot may send a pilot input at a press of abutton to capture current states of the outside environment andsubsystems of the electric aircraft to be displayed onto an outputdevice in pilot view. The captured current state may further display anew focal point based on that captured current state. In a non-limitingembodiment, controller 112 may condition signals such that they can besent and received by various components throughout the electric vehicle.In a non-limiting embodiment, flight datum may include at least anaircraft angle. At least an aircraft angle may include any informationabout the orientation of the aircraft in three-dimensional space such aspitch angle, roll angle, yaw angle, or some combination thereof. Innon-limiting examples, at least an aircraft angle may use one or morenotations or angular measurement systems like polar coordinates,cartesian coordinates, cylindrical coordinates, spherical coordinates,homogenous coordinates, relativistic coordinates, or a combinationthereof, among others. In a non-limiting embodiment, flight datum mayinclude at least an aircraft angle rate. At least an aircraft angle ratemay include any information about the rate of change of any angleassociated with an electrical aircraft as described herein. Anymeasurement system may be used in the description of at least anaircraft angle rate.

With continued reference to FIG. 4, receiving sensor datum may includereceiving from sensor in which a controller and/or sensor may include aplurality of physical controller area network buses communicativelyconnected to the aircraft and sensor 104. A “physical controller areanetwork bus,” as used in this disclosure, is vehicle bus unit includinga central processing unit (CPU), a CAN controller, and a transceiverdesigned to allow devices to communicate with each other's applicationswithout the need of a host computer which is located physically at theaircraft. Physical controller area network (CAN) bus unit may includephysical circuit elements that may use, for instance and withoutlimitation, twisted pair, digital circuit elements/FGPA,microcontroller, or the like to perform, without limitation, processingand/or signal transmission processes and/or tasks. In a non-limitingembodiment, the controller may receive the sensor datum from the sensorby a physical CAN bus unit. In a non-limiting embodiment, the sensor 104may include a physical CAN bus unit to detect sensor datum 108 in tandemwith a plurality of individual sensors from a sensor suite. Physical CANbus unit may include multiplex electrical wiring for transmission ofmultiplexed signaling. Physical CAN bus unit 104 may includemessage-based protocol(s), wherein the invoking program sends a messageto a process and relies on that process and its supportinginfrastructure to then select and run appropriate programing. Aplurality of physical CAN bus units may be located physically at theaircraft may include mechanical connection to the aircraft, wherein thehardware of the physical CAN bus unit is integrated within theinfrastructure of the aircraft.

With continued reference to FIG. 4, step 405 may include mapping thepilot inputs such as input datum, attitude such as at least an aircraftangle, and body angular rate measurement such as at least an aircraftangle rate to motor torque levels necessary to meet the input datum. Ina non-limiting exemplary embodiment, controller may include the nominalattitude command (ACAH) configuration, the controller may make thevehicle attitude track the pilot attitude while also applying thepilot-commanded amount of assisted lift and pusher torque which may beencapsulated within actuator allocation command datum. The flightcontroller is responsible only for mapping the pilot inputs, attitude,and body angular rate measurements to motor torque levels necessary tomeet the input datum. In the nominal attitude command (ACAH)configuration, controller makes the vehicle attitude track the pilotattitude while also applying the pilot commanded amount of assisted liftand pusher torque. In a non-limiting embodiment, controller may includethe calculation and control of avionics display of critical envelopeinformation i.e., stall warning, vortex ring state, pitch limitindicator, angle of attack, transition envelopes, etc. In a non-limitingembodiment, controller may calculate, command, and control trim assist,turn coordination, pitch to certain gravitational forces, automationintegration: attitude, position hold, LNAV, VNAV etc., minimum hoverthrust protection, angle of attack limits, etc., precision Autoland,other aspects of autopilot operations, advanced perception of obstaclesfor ‘see and avoid’ missions, and remote operations, among others.

Still referring to FIG. 4, method 400, at step 410, includes generatingan actuator performance model as a function of the sensor datum. In anon-limiting embodiment, actuator performance model may include a modeldepicting the performance of the aircraft in which one or more of theactuators are malfunctioning or failing. In a non-limiting embodiment,actuator performance model may be generated during a flight or after aflight has occurred. For example and without limitation, actuatorperformance model may depict the performance of the aircraft and theaircraft actuators in real time as it is flying in the air. In anon-limiting embodiment, actuator performance model may include adepiction of the flight of the aircraft. In a non-limiting embodiment,actuator performance model may include a plurality of performanceparameters include, but not limited to, aircraft velocity, attitude,actuator torque output, and the like thereof. In a non-limitingembodiment, actuator performance model may highlight an abnormality ofan actuator and a plurality of performance parameters associated withthat abnormal actuator. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of the various embodiments ofa simulation and/or model in the context of visualization and analysisconsistent with this disclosure.

With continued reference to FIG. 4, step 410 may include a flightsimulator to generate actuator performance model. In some cases, flightsimulator may simulate flight within an environment, for example anenvironmental atmosphere in which aircraft fly, airports at whichaircraft take-off and land, and/or mountains and other hazards aircraftattempt to avoid crashing into. In some cases, an environment mayinclude geographical, atmospheric, and/or biological features. In somecases, flight simulator 116 may model an artificial and/or virtualaircraft in flight as well as an environment in which the artificialand/or virtual aircraft flies. In some cases, flight simulator mayinclude one or more physics models, which represent analytically orthrough data-based, such as without limitation machine-learningprocesses, physical phenomenon. Physical phenomenon may be associatedwith an aircraft and/or an environment. For example, some versions offlight simulator may include thermal models representing aircraftcomponents by way of thermal modeling. Thermal modeling techniques may,in some cases, include analytical representation of one or more ofconvective hear transfer (for example by way of Newton's Law ofCooling), conductive heat transfer (for example by way of Fourierconduction), radiative heat transfer, and/or advective heat transfer. Insome cases, flight simulator may include models representing fluiddynamics. For example, in some embodiments, flight simulator may includea representation of turbulence, wind shear, air density, cloud,precipitation, and the like. In some embodiments, flight simulator mayinclude at least a model representing optical phenomenon. For example,flight simulator may include optical models representative oftransmission, reflectance, occlusion, absorption, attenuation, andscatter. Flight simulator may include non-analytical modeling methods;for example, the flight simulator may include, without limitation, aMonte Carlo model for simulating optical scatter within a turbid medium,for example clouds. In some embodiments, flight simulator may representNewtonian physics, for example motion, pressures, forces, moments, andthe like. An exemplary flight simulator may include Microsoft FlightSimulator from Microsoft of Redmond, Wash., U.S.A.

With continued reference to FIG. 4, method 400, at step 410, may includegenerating an expected actuator performance model as a function of theflight simulator. In a non-limiting embodiment, expected actuatorperformance model may include an actuator performance model that depictsa performance model in which none of the actuators are malfunctioning.For example and without limitation, expected actuator performance modelmay be a model depicting a performance of what the aircraft should bebased on the ideal, expected, or initial performance the aircraftactuators are intended to perform. For example and without limitation,expected actuator performance model includes peak performance outputincluding, but not limited to, power consumption, maximum torque output,cruising torque output, maximum attitude, cruising attitude, maximumvelocity, cruising velocity, and the like thereof. For example andwithout limitation, expected actuator performance model may highlightindividual performance parameters of each actuator based on a sensordisposed on each actuator. In a non-limiting embodiment, expectedactuator performance model can be used to assess the performance of theaircraft actuators by comparing expected actuator performance model toactuator performance model and analyzing the difference between the datafrom the two models. In a non-limiting embodiment, controller may feedflight simulator the ideal and/or peak performance parameters of anaircraft and its actuators to simulate expected actuator performancemodel based on those ideal and/or peak performance parameters. In anon-limiting embodiment, expected actuator performance model may includea plurality of expected actuator performance model depicting a differentfailure modes of an aircraft and/or an aircraft's actuators. For exampleand without limitation, a rotor may fail by outputting max thrust,outputting zero thrust, or be stuck at an intermediate setting. In someembodiments, models are determined based on and/or for various actuatorsettings. In various embodiments, only highly likely or relativelydangerous actuator failure modes are considered and modeled. Forexample, a rotor may be modeled for a zero-output case but not for apinned high case. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of the various models andcomparisons consistent with this disclosure.

Still referring to FIG. 4, method 400, at step 415, includes identifyinga defunct actuator of the electric aircraft as a function of the sensordatum and the actuator performance model. In a non-limiting embodiment,defunct actuator may include any actuator that may produce abnormaloutputs. For example and without limitation, defunct actuator may outputa torque of 1.5 Newton-metre (Nm) while the remaining actuators mayoutput a torque of 3.6 Nm. In a non-limiting embodiment, actuators mayinclude different failure modes which are represented by variousexpected actuator performance model. For example, a rotor may fail byoutputting max thrust, outputting zero thrust, or be stuck at anintermediate setting. In some embodiments, models are determined basedon and/or for various actuator settings. In various embodiments, onlyhighly likely or relatively dangerous actuator failure modes areconsidered and modeled. For example, a rotor may be modeled for azero-output case but not for a pinned high case.

With continued reference to FIG. 4, method 400, at step 415, may includecomparing actuator performance model to expected actuator performancemodel to identify defunct actuator. For example and without limitation,controller may sort models based on their expected metrics and selectthe model that has expected metrics that closely match actual orobserved metrics. In one example, the metrics compared include attitudeand rates of change in attitude of the aircraft. In some embodiments,controller may compares observed metrics of the aircraft to expectedmetrics of the aircraft in an operable mode (e.g. no actuator failuresassociated with a “no failure” model). In a non-limiting embodiment,controller may receive from flight simulator 116, a plurality ofexpected actuator performance model which may include a model for everypossible failure mode. For example and without limitation, in anaircraft comprising four rotors, a model is determined for a first rotorfailure, a second rotor failure, a third rotor failure, a fourth rotorfailure, a first and second rotor failure, a first and third rotorfailure, a first and fourth rotor failure, a second and third rotorfailure, and a second and fourth rotor failure. Multiple additionalmodels may be determined including a model for no rotor failure and allrotor failure in addition to a first, second, and third rotor failureand a second, third, and fourth rotor failure. In some embodiments, thenumber of models determined is equal to the number of actuators squaredplus one. In some embodiments, the number of considered failure modes isless than the total possible failure modes. For example, models may notbe determined for less likely failure modes in order to limitcomputations performed. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various embodiments offailure modes used to identify a defunct actuator consistent with thisdisclosure. In a non-limiting embodiment, the plurality of expectedactuator performance model 120 may be sorted based on similarity to anobserved flight datum. For example, an expected flight datum includingexpected attitude and expected attitude rate given a failure mode isdetermined or otherwise generated based on and/or for each model. Insome embodiments, the models are sorted based on how closely theircorresponding expected attitude and expected attitude rate matches theobserved attitude and attitude rate. The sorted models may result in asorted list of failure modes from most likely to least likely. Forexample, in the event a first rotor failure mode has a correspondingexpected attitude that is 0.2 off from the observed attitude and anexpected attitude rate that is 0.1 off from the observed attitude ratewhereas a second rotor failure mode has a corresponding expectedattitude that is 0.7 off from the observed attitude and an expectedattitude rate that is 0.9 off from the observed attitude, the firstrotor failure is determined to be more likely than the second rotorfailure mode. In some embodiments, the summation of the differencebetween expected attitude and observed attitude and the differencebetween expected attitude rate and observed attitude rate is used tosort the models. In some embodiments, expected attitude is weighted morethan expected attitude rate or vice versa. A model's similarity toobserved metrics may be determined using various calculations based onthe expected and observed values in various embodiments. In anon-limiting embodiment, controller may identify defunct actuator or oneor more defunct actuators based on the sorted model. For example, thefailure mode top of the sorted list may be selected. Actuator failuresthat correspond to the failure mode are determined to be in effect.

Still referring to FIG. 4, method 400, at step 420, includes generatingan actuator allocation command datum as a function of at least theactuator performance model and at least the identification of thedefunct actuator. Generating actuator allocation command datum mayinclude generating actuator allocation command as a function of amachine-learning model. Generating actuator allocation command datum mayinclude, in part, using an outer loop controller, wherein outer loopcontroller may be configured to generate rate setpoint as a function ofsensor datum and the identification of defunct actuator. Rate setpointmay include any rate setpoint as described herein. Outer loop controllermay include one or more computing devices consistent with thisdisclosure and/or one or more components and/or modules thereof.

Still referring to FIG. 4, method 400, at step 420, may include outerloop controller generating rate setpoint as a function of sensor datumand the identification of defunct actuator. Outer loop controller mayinclude circuitry, components, processors, transceivers, or acombination thereof configured to receive and/or send electricalsignals. Outer loop controller may include aproportional-integral-derivative (PID) controller. PID controllers mayautomatically apply accurate and responsive correction to a controlfunction in a loop, such that over time the correction remainsresponsive to the previous output and actively controls an output.Controller may include damping, including critical damping to attain thedesired setpoint, which may be an output to a propulsor in a timely andaccurate way. Outer loop controller may include components, circuitry,receivers, transceivers, or a combination thereof. Outer loop controllermay be configured to generate rate setpoint 136 as a function of sensordatum and identification of defunct actuator. In a non-limitingembodiment, controller may use an outer angle loop driving an inner rateloop to provide closed loop control with setpoints of desired pitchattitude, roll attitude, and yaw rate provided directly by the pilot.The outer (angle) loop provides rate setpoint. Rate setpoint may includethe desired rate of change of one or more angles describing theaircraft's orientation, heading, and propulsion, or a combinationthereof. Rate setpoint may include the pilot's desired rate of change ofaircraft pitch angle, consistent with pitch angles, and largely at leastan aircraft angle in the entirety of this disclosure. Rate setpoint mayinclude a measurement in a plurality of measurement systems includingquaternions or any other measurement system as described herein.

Still referring to FIG. 4, method 400, at step 420, may include using aninner loop controller, wherein inner loop controller may be configuredto generate moment datum as a function of rate setpoint. Inner loopcontroller may include any inner loop controller as described herein.Moment datum may include any moment datum as described herein. Momentdatum may include any information describing the moment of an aircraft.Moment datum includes information regarding pilot's desire to apply acertain moment or collection of moments on one or more portions of anelectric aircraft, including the entirety of the aircraft.

Still referring to FIG. 4, method 400, at step 420, may include using amixer, wherein the mixer may be configured to generate actuatorallocation command datum as a function of moment datum. The mixer mayinclude any mixer as described herein. In a non-limiting embodiment,moment datum may include a plurality of attitude commands and allocatesone or more outgoing signals, such as modified attitude commands andoutput torque command, or the like, to at least a propulsor, flightcomponent, or one or more computing devices connected thereto. In anon-limiting embodiment, the mixer may be configured to solve at leastan optimization problem, which may be an objective function. Objectivefunction may include any objective function as described herein. Themixer may compute a score, metric, ranking, or the like, associated witheach performance prognoses and candidate transfer apparatus and selectobjectives to minimize and/or maximize the score/rank, depending onwhether an optimal result may be represented, respectively, by a minimaland/or maximal score; an objective function may be used by mixer toscore each possible pairing. At least an optimization problem may bebased on one or more objectives. In a non-limiting embodiment, the mixermay be configured to generate actuator allocation command datum as afunction of the torque allocation. Actuator allocation command datum mayinclude at least a torque vector. Actuator allocation command datum maybe represented in any suitable form, which may include, withoutlimitation, vectors, matrices, coefficients, scores, ranks, or othernumerical comparators, and the like.

Still referring to FIG. 4, method 400, at step 425, includes performinga torque allocation as a function of the actuator allocation commanddatum. In a non-limiting embodiment, performing the torque allocationmay include commanding one or more actuators to apply a specific amountof torque. In a non-limiting embodiment, actuator allocation commanddatum may include unique torque allocations for each actuator to beexecuted. For example and without limitation, actuator allocationcommand datum may instruct each functioning actuator to allocate atorque output of 4 Nm and instruct defunct actuator 128 to allocate atorque output of 0.4 Nm. For example and without limitation, actuatorallocation command datum may instruct one or more defunct actuators tocommand a torque of 0 Nm and the remaining functioning actuators atorque of 6 Nm. In a non-limiting embodiment, actuator allocationcommand datum may be generated as a function of a torque allocation.

Referring now to FIG. 5, an exemplary embodiment of an aircraft 500,which may include, or be incorporated with, a system for optimization ofa recharging flight plan is illustrated. As used in this disclosure an“aircraft” is any vehicle that may fly by gaining support from the air.As a non-limiting example, aircraft may include airplanes, helicopters,commercial and/or recreational aircrafts, instrument flight aircrafts,drones, electric aircrafts, airliners, rotorcrafts, vertical takeoff andlanding aircrafts, jets, airships, blimps, gliders, paramotors, and thelike thereof.

Still referring to FIG. 5, aircraft 500 may include an electricallypowered aircraft. In embodiments, electrically powered aircraft may bean electric vertical takeoff and landing (eVTOL) aircraft. Aircraft 500may include an unmanned aerial vehicle and/or a drone. Electric aircraftmay be capable of rotor-based cruising flight, rotor-based takeoff,rotor-based landing, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Electricaircraft may include one or more manned and/or unmanned aircrafts.Electric aircraft may include one or more all-electric short takeoff andlanding (eSTOL) aircrafts. For example, and without limitation, eSTOLaircrafts may accelerate the plane to a flight speed on takeoff anddecelerate the plane after landing. In an embodiment, and withoutlimitation, electric aircraft may be configured with an electricpropulsion assembly. Electric propulsion assembly may include anyelectric propulsion assembly as described in U.S. Nonprovisionalapplication Ser. No. 16/703,225, and entitled “AN INTEGRATED ELECTRICPROPULSION ASSEMBLY,” the entirety of which is incorporated herein byreference. For purposes of description herein, the terms “upper”,“lower”, “left”, “rear”, “right”, “front”, “vertical”, “horizontal”,“upward”, “downward”, “forward”, “backward” and derivatives thereofshall relate to the invention as oriented in FIG. 5.

Still referring to FIG. 5, aircraft 500 includes a fuselage 504. As usedin this disclosure a “fuselage” is the main body of an aircraft, or inother words, the entirety of the aircraft except for the cockpit, nose,wings, empennage, nacelles, any and all control surfaces, and generallycontains an aircraft's payload. Fuselage 504 may include structuralelements that physically support a shape and structure of an aircraft.Structural elements may take a plurality of forms, alone or incombination with other types. Structural elements may vary depending ona construction type of aircraft such as without limitation a fuselage504. Fuselage 504 may comprise a truss structure. A truss structure maybe used with a lightweight aircraft and comprises welded steel tubetrusses. A “truss,” as used in this disclosure, is an assembly of beamsthat create a rigid structure, often in combinations of triangles tocreate three-dimensional shapes. A truss structure may alternativelycomprise wood construction in place of steel tubes, or a combinationthereof. In embodiments, structural elements may comprise steel tubesand/or wood beams. In an embodiment, and without limitation, structuralelements may include an aircraft skin. Aircraft skin may be layered overthe body shape constructed by trusses. Aircraft skin may comprise aplurality of materials such as plywood sheets, aluminum, fiberglass,and/or carbon fiber, the latter of which will be addressed in greaterdetail later herein.

In embodiments, and with continued reference to FIG. 5, aircraftfuselage 504 may include and/or be constructed using geodesicconstruction. Geodesic structural elements may include stringers woundabout formers (which may be alternatively called station frames) inopposing spiral directions. A “stringer,” as used in this disclosure, isa general structural element that includes a long, thin, and rigid stripof metal or wood that is mechanically coupled to and spans a distancefrom, station frame to station frame to create an internal skeleton onwhich to mechanically couple aircraft skin. A former (or station frame)may include a rigid structural element that is disposed along a lengthof an interior of aircraft fuselage 504 orthogonal to a longitudinal(nose to tail) axis of the aircraft and may form a general shape offuselage 504. A former may include differing cross-sectional shapes atdiffering locations along fuselage 504, as the former is the structuralelement that informs the overall shape of a fuselage 504 curvature. Inembodiments, aircraft skin may be anchored to formers and strings suchthat the outer mold line of a volume encapsulated by formers andstringers comprises the same shape as aircraft 500 when installed. Inother words, former(s) may form a fuselage's ribs, and the stringers mayform the interstitials between such ribs. The spiral orientation ofstringers about formers may provide uniform robustness at any point onan aircraft fuselage such that if a portion sustains damage, anotherportion may remain largely unaffected. Aircraft skin may be mechanicallycoupled to underlying stringers and formers and may interact with afluid, such as air, to generate lift and perform maneuvers.

In an embodiment, and still referring to FIG. 5, fuselage 504 mayinclude and/or be constructed using monocoque construction. Monocoqueconstruction may include a primary structure that forms a shell (or skinin an aircraft's case) and supports physical loads. Monocoque fuselagesare fuselages in which the aircraft skin or shell is also the primarystructure. In monocoque construction aircraft skin would support tensileand compressive loads within itself and true monocoque aircraft can befurther characterized by the absence of internal structural elements.Aircraft skin in this construction method is rigid and can sustain itsshape with no structural assistance form underlying skeleton-likeelements. Monocoque fuselage may comprise aircraft skin made fromplywood layered in varying grain directions, epoxy-impregnatedfiberglass, carbon fiber, or any combination thereof.

According to embodiments, and further referring to FIG. 5, fuselage 504may include a semi-monocoque construction. Semi-monocoque construction,as used herein, is a partial monocoque construction, wherein a monocoqueconstruction is describe above detail. In semi-monocoque construction,aircraft fuselage 504 may derive some structural support from stressedaircraft skin and some structural support from underlying framestructure made of structural elements. Formers or station frames can beseen running transverse to the long axis of fuselage 504 with circularcutouts which are generally used in real-world manufacturing for weightsavings and for the routing of electrical harnesses and other modernon-board systems. In a semi-monocoque construction, stringers are thin,long strips of material that run parallel to fuselage's long axis.Stringers may be mechanically coupled to formers permanently, such aswith rivets. Aircraft skin may be mechanically coupled to stringers andformers permanently, such as by rivets as well. A person of ordinaryskill in the art will appreciate, upon reviewing the entirety of thisdisclosure, that there are numerous methods for mechanical fastening ofthe aforementioned components like screws, nails, dowels, pins, anchors,adhesives like glue or epoxy, or bolts and nuts, to name a few. A subsetof fuselage under the umbrella of semi-monocoque construction includesunibody vehicles. Unibody, which is short for “unitized body” oralternatively “unitary construction”, vehicles are characterized by aconstruction in which the body, floor plan, and chassis form a singlestructure. In the aircraft world, unibody may be characterized byinternal structural elements like formers and stringers beingconstructed in one piece, integral to the aircraft skin as well as anyfloor construction like a deck.

Still referring to FIG. 5, stringers and formers, which may account forthe bulk of an aircraft structure excluding monocoque construction, maybe arranged in a plurality of orientations depending on aircraftoperation and materials. Stringers may be arranged to carry axial(tensile or compressive), shear, bending or torsion forces throughouttheir overall structure. Due to their coupling to aircraft skin,aerodynamic forces exerted on aircraft skin will be transferred tostringers. A location of said stringers greatly informs the type offorces and loads applied to each and every stringer, all of which may behandled by material selection, cross-sectional area, and mechanicalcoupling methods of each member. A similar assessment may be made forformers. In general, formers may be significantly larger incross-sectional area and thickness, depending on location, thanstringers. Both stringers and formers may comprise aluminum, aluminumalloys, graphite epoxy composite, steel alloys, titanium, or anundisclosed material alone or in combination.

In an embodiment, and still referring to FIG. 5, stressed skin, whenused in semi-monocoque construction is the concept where the skin of anaircraft bears partial, yet significant, load in an overall structuralhierarchy. In other words, an internal structure, whether it be a frameof welded tubes, formers and stringers, or some combination, may not besufficiently strong enough by design to bear all loads. The concept ofstressed skin may be applied in monocoque and semi-monocoqueconstruction methods of fuselage 504. Monocoque comprises onlystructural skin, and in that sense, aircraft skin undergoes stress byapplied aerodynamic fluids imparted by the fluid. Stress as used incontinuum mechanics may be described in pound-force per square inch(lbf/in²) or Pascals (Pa). In semi-monocoque construction stressed skinmay bear part of aerodynamic loads and additionally may impart force onan underlying structure of stringers and formers.

Still referring to FIG. 5, it should be noted that an illustrativeembodiment is presented only, and this disclosure in no way limits theform or construction method of a system and method for loading payloadinto an eVTOL aircraft. In embodiments, fuselage 504 may be configurablebased on the needs of the eVTOL per specific mission or objective. Thegeneral arrangement of components, structural elements, and hardwareassociated with storing and/or moving a payload may be added or removedfrom fuselage 504 as needed, whether it is stowed manually, automatedly,or removed by personnel altogether. Fuselage 504 may be configurable fora plurality of storage options. Bulkheads and dividers may be installedand uninstalled as needed, as well as longitudinal dividers wherenecessary. Bulkheads and dividers may be installed using integratedslots and hooks, tabs, boss and channel, or hardware like bolts, nuts,screws, nails, clips, pins, and/or dowels, to name a few. Fuselage 504may also be configurable to accept certain specific cargo containers, ora receptacle that can, in turn, accept certain cargo containers.

Still referring to FIG. 5, aircraft 500 may include a plurality oflaterally extending elements attached to fuselage 504. As used in thisdisclosure a “laterally extending element” is an element that projectsessentially horizontally from fuselage, including an outrigger, a spar,and/or a fixed wing that extends from fuselage. Wings may be structureswhich include airfoils configured to create a pressure differentialresulting in lift. Wings may generally dispose on the left and rightsides of the aircraft symmetrically, at a point between nose andempennage. Wings may comprise a plurality of geometries in planformview, swept swing, tapered, variable wing, triangular, oblong,elliptical, square, among others. A wing's cross section geometry maycomprise an airfoil. An “airfoil” as used in this disclosure is a shapespecifically designed such that a fluid flowing above and below it exertdiffering levels of pressure against the top and bottom surface. Inembodiments, the bottom surface of an aircraft can be configured togenerate a greater pressure than does the top, resulting in lift.Laterally extending element may comprise differing and/or similarcross-sectional geometries over its cord length or the length from wingtip to where wing meets the aircraft's body. One or more wings may besymmetrical about the aircraft's longitudinal plane, which comprises thelongitudinal or roll axis reaching down the center of the aircraftthrough the nose and empennage, and the plane's yaw axis. Laterallyextending element may comprise controls surfaces configured to becommanded by a pilot or pilots to change a wing's geometry and thereforeits interaction with a fluid medium, like air. Control surfaces maycomprise flaps, ailerons, tabs, spoilers, and slats, among others. Thecontrol surfaces may dispose on the wings in a plurality of locationsand arrangements and in embodiments may be disposed at the leading andtrailing edges of the wings, and may be configured to deflect up, down,forward, aft, or a combination thereof. An aircraft, including adual-mode aircraft may comprise a combination of control surfaces toperform maneuvers while flying or on ground.

Still referring to FIG. 5, aircraft 500 includes a plurality of flightcomponents 508. As used in this disclosure a “flight component” is acomponent that promotes flight and guidance of an aircraft. In anembodiment, flight component 508 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling caninclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling may be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling may be used to join two pieces ofrotating electric aircraft components.

Still referring to FIG. 5, plurality of flight components 508 mayinclude at least a lift propulsor component 512. As used in thisdisclosure a “lift propulsor component” is a component and/or deviceused to propel a craft upward by exerting downward force on a fluidmedium, which may include a gaseous medium such as air or a liquidmedium such as water. Lift propulsor component 512 may include anydevice or component that consumes electrical power on demand to propelan electric aircraft in a direction or other vehicle while on ground orin-flight. For example, and without limitation, lift propulsor component512 may include a rotor, propeller, paddle wheel and the like thereof,wherein a rotor is a component that produces torque along thelongitudinal axis, and a propeller produces torquer along the verticalaxis. In an embodiment, lift propulsor component 512 includes aplurality of blades. As used in this disclosure a “blade” is a propellerthat converts rotary motion from an engine or other power source into aswirling slipstream. In an embodiment, blade may convert rotary motionto push the propeller forwards or backwards. In an embodiment liftpropulsor component 512 may include a rotating power-driven hub, towhich are attached several radial airfoil-section blades such that thewhole assembly rotates about a longitudinal axis. Blades may beconfigured at an angle of attack, wherein an angle of attack isdescribed in detail below. In an embodiment, and without limitation,angle of attack may include a fixed angle of attack. As used in thisdisclosure a “fixed angle of attack” is fixed angle between a chord lineof a blade and relative wind. As used in this disclosure a “fixed angle”is an angle that is secured and/or unmovable from the attachment point.For example, and without limitation fixed angle of attack may be 3.2° asa function of a pitch angle of 19.7° and a relative wind angle 16.5°. Inanother embodiment, and without limitation, angle of attack may includea variable angle of attack. As used in this disclosure a “variable angleof attack” is a variable and/or moveable angle between a chord line of ablade and relative wind. As used in this disclosure a “variable angle”is an angle that is moveable from an attachment point. For example, andwithout limitation variable angle of attack may be a first angle of 4.7°as a function of a pitch angle of 17.1° and a relative wind angle 16.4°,wherein the angle adjusts and/or shifts to a second angle of 16.7° as afunction of a pitch angle of 16.1° and a relative wind angle 16.4°. Inan embodiment, angle of attack be configured to produce a fixed pitchangle. As used in this disclosure a “fixed pitch angle” is a fixed anglebetween a cord line of a blade and the rotational velocity direction.For example, and without limitation, fixed pitch angle may include 18°.In another embodiment fixed angle of attack may be manually variable toa few set positions to adjust one or more lifts of the aircraft prior toflight. In an embodiment, blades for an aircraft are designed to befixed to their hub at an angle similar to the thread on a screw makes anangle to the shaft; this angle may be referred to as a pitch or pitchangle which will determine a speed of forward movement as the bladerotates.

In an embodiment, and still referring to FIG. 5, lift propulsorcomponent 512 may be configured to produce a lift. As used in thisdisclosure a “lift” is a perpendicular force to the oncoming flowdirection of fluid surrounding the surface. For example, and withoutlimitation relative air speed may be horizontal to aircraft 500, whereinlift force may be a force exerted in a vertical direction, directingaircraft 500 upwards. In an embodiment, and without limitation, liftpropulsor component 512 may produce lift as a function of applying atorque to lift propulsor component. As used in this disclosure a“torque” is a measure of force that causes an object to rotate about anaxis in a direction. For example, and without limitation, torque mayrotate an aileron and/or rudder to generate a force that may adjustand/or affect altitude, airspeed velocity, groundspeed velocity,direction during flight, and/or thrust. For example, one or more flightcomponents such as a power sources may apply a torque on lift propulsorcomponent 512 to produce lift. As used in this disclosure a “powersource” is a source that that drives and/or controls any other flightcomponent. For example, and without limitation power source may includea motor that operates to move one or more lift propulsor components, todrive one or more blades, or the like thereof. A motor may be driven bydirect current (DC) electric power and may include, without limitation,brushless DC electric motors, switched reluctance motors, inductionmotors, or any combination thereof. A motor may also include electronicspeed controllers or other components for regulating motor speed,rotation direction, and/or dynamic braking.

Still referring to FIG. 5, power source may include an energy source. Anenergy source may include, for example, an electrical energy source agenerator, a photovoltaic device, a fuel cell such as a hydrogen fuelcell, direct methanol fuel cell, and/or solid oxide fuel cell, anelectric energy storage device (e.g., a capacitor, an inductor, and/or abattery). An electrical energy source may also include a battery cell,or a plurality of battery cells connected in series into a module andeach module connected in series or in parallel with other modules.Configuration of an energy source containing connected modules may bedesigned to meet an energy or power requirement and may be designed tofit within a designated footprint in an electric aircraft in whichaircraft 500 may be incorporated.

In an embodiment, and still referring to FIG. 5, an energy source may beused to provide a steady supply of electrical power to a load over thecourse of a flight by a vehicle or other electric aircraft. For example,an energy source may be capable of providing sufficient power for“cruising” and other relatively low-energy phases of flight. An energysource may also be capable of providing electrical power for somehigher-power phases of flight as well, particularly when the energysource is at a high SOC, as may be the case for instance during takeoff.In an embodiment, an energy source may be capable of providingsufficient electrical power for auxiliary loads including withoutlimitation, lighting, navigation, communications, de-icing, steering orother systems requiring power or energy. Further, an energy source maybe capable of providing sufficient power for controlled descent andlanding protocols, including, without limitation, hovering descent orrunway landing. As used herein an energy source may have high powerdensity where electrical power an energy source can usefully produce perunit of volume and/or mass is relatively high. “Electrical power,” asused in this disclosure, is defined as a rate of electrical energy perunit time. An energy source may include a device for which power thatmay be produced per unit of volume and/or mass has been optimized, atthe expense of the maximal total specific energy density or powercapacity, during design. Non-limiting examples of items that may be usedas at least an energy source may include batteries used for startingapplications including Li ion batteries which may include NCA, NMC,Lithium iron phosphate (LiFePO4) and Lithium Manganese Oxide (LMO)batteries, which may be mixed with another cathode chemistry to providemore specific power if the application requires Li metal batteries,which have a lithium metal anode that provides high power on demand, Liion batteries that have a silicon or titanite anode, energy source maybe used, in an embodiment, to provide electrical power to an electricaircraft or drone, such as an electric aircraft vehicle, during momentsrequiring high rates of power output, including without limitationtakeoff, landing, thermal de-icing and situations requiring greaterpower output for reasons of stability, such as high turbulencesituations, as described in further detail below. A battery may include,without limitation a battery using nickel based chemistries such asnickel cadmium or nickel metal hydride, a battery using lithium ionbattery chemistries such as a nickel cobalt aluminum (NCA), nickelmanganese cobalt (NMC), lithium iron phosphate (LiFePO4), lithium cobaltoxide (LCO), and/or lithium manganese oxide (LMO), a battery usinglithium polymer technology, lead-based batteries such as withoutlimitation lead acid batteries, metal-air batteries, or any othersuitable battery. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 5, an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Amodule may include batteries connected in parallel or in series or aplurality of modules connected either in series or in parallel designedto deliver both the power and energy requirements of the application.Connecting batteries in series may increase the voltage of at least anenergy source which may provide more power on demand. High voltagebatteries may require cell matching when high peak load is needed. Asmore cells are connected in strings, there may exist the possibility ofone cell failing which may increase resistance in the module and reducean overall power output as a voltage of the module may decrease as aresult of that failing cell. Connecting batteries in parallel mayincrease total current capacity by decreasing total resistance, and italso may increase overall amp-hour capacity. Overall energy and poweroutputs of at least an energy source may be based on individual batterycell performance or an extrapolation based on measurement of at least anelectrical parameter. In an embodiment where an energy source includes aplurality of battery cells, overall power output capacity may bedependent on electrical parameters of each individual cell. If one cellexperiences high self-discharge during demand, power drawn from at leastan energy source may be decreased to avoid damage to the weakest cell.An energy source may further include, without limitation, wiring,conduit, housing, cooling system and battery management system. Personsskilled in the art will be aware, after reviewing the entirety of thisdisclosure, of many different components of an energy source.

In an embodiment and still referring to FIG. 5, plurality of flightcomponents 508 may be arranged in a quad copter orientation. As used inthis disclosure a “quad copter orientation” is at least a lift propulsorcomponent oriented in a geometric shape and/or pattern, wherein each ofthe lift propulsor components are located along a vertex of thegeometric shape. For example, and without limitation, a square quadcopter orientation may have four lift propulsor components oriented inthe geometric shape of a square, wherein each of the four lift propulsorcomponents are located along the four vertices of the square shape. As afurther non-limiting example, a hexagonal quad copter orientation mayhave six lift propulsor components oriented in the geometric shape of ahexagon, wherein each of the six lift propulsor components are locatedalong the six vertices of the hexagon shape. In an embodiment, andwithout limitation, quad copter orientation may include a first set oflift propulsor components and a second set of lift propulsor components,wherein the first set of lift propulsor components and the second set oflift propulsor components may include two lift propulsor componentseach, wherein the first set of lift propulsor components and a secondset of lift propulsor components are distinct from one another. Forexample, and without limitation, the first set of lift propulsorcomponents may include two lift propulsor components that rotate in aclockwise direction, wherein the second set of lift propulsor componentsmay include two lift propulsor components that rotate in acounterclockwise direction. In an embodiment, and without limitation,the first set of propulsor lift components may be oriented along a lineoriented 45° from the longitudinal axis of aircraft 500. In anotherembodiment, and without limitation, the second set of propulsor liftcomponents may be oriented along a line oriented 135° from thelongitudinal axis, wherein the first set of lift propulsor componentsline and the second set of lift propulsor components are perpendicularto each other.

Still referring to FIG. 5, plurality of flight components 508 mayinclude a pusher component 516. As used in this disclosure a “pushercomponent” is a component that pushes and/or thrusts an aircraft througha medium. As a non-limiting example, pusher component 516 may include apusher propeller, a paddle wheel, a pusher motor, a pusher propulsor,and the like. Additionally, or alternatively, pusher flight componentmay include a plurality of pusher flight components. Pusher component516 is configured to produce a forward thrust. As used in thisdisclosure a “forward thrust” is a thrust that forces aircraft through amedium in a horizontal direction, wherein a horizontal direction is adirection parallel to the longitudinal axis. As a non-limiting example,forward thrust may include a force of 1145 N to force aircraft to in ahorizontal direction along the longitudinal axis. As a furthernon-limiting example, forward thrust may include a force of, as anon-limiting example, 300 N to force aircraft 500 in a horizontaldirection along a longitudinal axis. As a further non-limiting example,pusher component 516 may twist and/or rotate to pull air behind it and,at the same time, push aircraft 500 forward with an equal amount offorce. In an embodiment, and without limitation, the more air forcedbehind aircraft, the greater the thrust force with which the aircraft ispushed horizontally will be. In another embodiment, and withoutlimitation, forward thrust may force aircraft 500 through the medium ofrelative air. Additionally or alternatively, plurality of flightcomponents 508 may include one or more puller components. As used inthis disclosure a “puller component” is a component that pulls and/ortows an aircraft through a medium. As a non-limiting example, pullercomponent may include a flight component such as a puller propeller, apuller motor, a tractor propeller, a puller propulsor, and the like.Additionally, or alternatively, puller component may include a pluralityof puller flight components.

In an embodiment and still referring to FIG. 5, aircraft 500 may includea flight controller located within fuselage 504, wherein a flightcontroller is described in detail below, in reference to FIG. 5. In anembodiment, and without limitation, flight controller may be configuredto operate a fixed-wing flight capability. As used in this disclosure a“fixed-wing flight capability” is a method of flight wherein theplurality of laterally extending elements generate lift. For example,and without limitation, fixed-wing flight capability may generate liftas a function of an airspeed of aircraft 100 and one or more airfoilshapes of the laterally extending elements, wherein an airfoil isdescribed above in detail. As a further non-limiting example, flightcontroller may operate the fixed-wing flight capability as a function ofreducing applied torque on lift propulsor component 512. For example,and without limitation, flight controller may reduce a torque of 19 Nmapplied to a first set of lift propulsor components to a torque of 16Nm. As a further non-limiting example, flight controller may reduce atorque of 12 Nm applied to a first set of lift propulsor components to atorque of 0 Nm. In an embodiment, and without limitation, flightcontroller may produce fixed-wing flight capability as a function ofincreasing forward thrust exerted by pusher component 516. For example,and without limitation, flight controller may increase a forward thrustof 500 kN produced by pusher component 516 to a forward thrust of 1669kN. In an embodiment, and without limitation, an amount of liftgeneration may be related to an amount of forward thrust generated toincrease airspeed velocity, wherein the amount of lift generation may bedirectly proportional to the amount of forward thrust produced.Additionally or alternatively, flight controller may include an inertiacompensator. As used in this disclosure an “inertia compensator” is oneor more computing devices, electrical components, logic circuits,processors, and the like there of that are configured to compensate forinertia in one or more lift propulsor components present in aircraft500. Inertia compensator may alternatively or additionally include anycomputing device used as an inertia compensator as described in U.S.Nonprovisional application Ser. No. 17/106,557, and entitled “SYSTEM ANDMETHOD FOR FLIGHT CONTROL IN ELECTRIC AIRCRAFT,” the entirety of whichis incorporated herein by reference.

In an embodiment, and still referring to FIG. 5, flight controller maybe configured to perform a reverse thrust command. As used in thisdisclosure a “reverse thrust command” is a command to perform a thrustthat forces a medium towards the relative air opposing aircraft 190. Forexample, reverse thrust command may include a thrust of 180 N directedtowards the nose of aircraft to at least repel and/or oppose therelative air. Reverse thrust command may alternatively or additionallyinclude any reverse thrust command as described in U.S. Nonprovisionalapplication Ser. No. 17/319,155 and entitled “AIRCRAFT HAVING REVERSETHRUST CAPABILITIES,” the entirety of which is incorporated herein byreference. In another embodiment, flight controller may be configured toperform a regenerative drag operation. As used in this disclosure a“regenerative drag operation” is an operating condition of an aircraft,wherein the aircraft has a negative thrust and/or is reducing inairspeed velocity. For example, and without limitation, regenerativedrag operation may include a positive propeller speed and a negativepropeller thrust. Regenerative drag operation may alternatively oradditionally include any regenerative drag operation as described inU.S. Nonprovisional application Ser. No. 17/319,155.

In an embodiment, and still referring to FIG. 5, flight controller maybe configured to perform a corrective action as a function of a failureevent. As used in this disclosure a “corrective action” is an actionconducted by the plurality of flight components to correct and/or altera movement of an aircraft. For example, and without limitation, acorrective action may include an action to reduce a yaw torque generatedby a failure event. Additionally or alternatively, corrective action mayinclude any corrective action as described in U.S. Nonprovisionalapplication Ser. No. 17/222,539, and entitled “AIRCRAFT FORSELF-NEUTRALIZING FLIGHT,” the entirety of which is incorporated hereinby reference. As used in this disclosure a “failure event” is a failureof a lift propulsor component of the plurality of lift propulsorcomponents. For example, and without limitation, a failure event maydenote a rotation degradation of a rotor, a reduced torque of a rotor,and the like thereof.

Now referring to FIG. 6, an exemplary embodiment 600 of a flightcontroller 604 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 604 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 604may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 604 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 6, flight controller 604may include a signal transformation component 608. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 608 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component608 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 19-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 608 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 608 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 608 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 6, signal transformation component 608 may beconfigured to optimize an intermediate representation 612. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 608 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 608 may optimizeintermediate representation 612 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 608 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 608 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 604. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 608 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 6, flight controller 604may include a reconfigurable hardware platform 616. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 616 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 6, reconfigurable hardware platform 616 mayinclude a logic component 620. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 620 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 620 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 620 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 620 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 620 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 612. Logiccomponent 620 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 604. Logiccomponent 620 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 620 may beconfigured to execute the instruction on intermediate representation 612and/or output language. For example, and without limitation, logiccomponent 620 may be configured to execute an addition operation onintermediate representation 612 and/or output language.

In an embodiment, and without limitation, logic component 620 may beconfigured to calculate a flight element 624. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 624 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 624 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 624 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 6, flight controller 604 may include a chipsetcomponent 628. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 628 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 620 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 628 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 620 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 628 maymanage data flow between logic component 620, memory cache, and a flightcomponent. As used in this disclosure a “flight component” is a portionof an aircraft that can be moved or adjusted to affect one or moreflight elements. For example, flight component may include a componentused to affect the aircrafts' roll and pitch which may comprise one ormore ailerons. As a further example, flight component may include arudder to control yaw of an aircraft. In an embodiment, chipsetcomponent 628 may be configured to communicate with a plurality offlight components as a function of flight element 624. For example, andwithout limitation, chipset component 628 may transmit to an aircraftrotor to reduce torque of a first lift propulsor and increase theforward thrust produced by a pusher component to perform a flightmaneuver.

In an embodiment, and still referring to FIG. 6, flight controller 604may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 604 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 624. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 604 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 604 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 6, flight controller 604may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 624 and a pilot signal636 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 636may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 636 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 636may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 636 may include an explicitsignal directing flight controller 604 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 636 may include an implicit signal, wherein flight controller 604detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 636 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 636 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 636 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 636 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal636 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 6, autonomous machine-learning model may includeone or more autonomous machine-learning processes such as supervised,unsupervised, or reinforcement machine-learning processes that flightcontroller 604 and/or a remote device may or may not use in thegeneration of autonomous function. As used in this disclosure “remotedevice” is an external device to flight controller 604. Additionally oralternatively, autonomous machine-learning model may include one or moreautonomous machine-learning processes that a field-programmable gatearray (FPGA) may or may not use in the generation of autonomousfunction. Autonomous machine-learning process may include, withoutlimitation machine learning processes such as simple linear regression,multiple linear regression, polynomial regression, support vectorregression, ridge regression, lasso regression, elasticnet regression,decision tree regression, random forest regression, logistic regression,logistic classification, K-nearest neighbors, support vector machines,kernel support vector machines, naïve bayes, decision treeclassification, random forest classification, K-means clustering,hierarchical clustering, dimensionality reduction, principal componentanalysis, linear discriminant analysis, kernel principal componentanalysis, Q-learning, State Action Reward State Action (SARSA), Deep-Qnetwork, Markov decision processes, Deep Deterministic Policy Gradient(DDPG), or the like thereof.

In an embodiment, and still referring to FIG. 6, autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 604 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 6, flight controller 604 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 604. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 604 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 604 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 6, flight controller 604 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 6, flight controller 604may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller604 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 604 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 604 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 6, control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 6, the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 604. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 612 and/or output language from logiccomponent 620, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 6, master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 6, control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 6, flight controller 604 may also be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of aircraft and/orcomputing device. Flight controller 604 may include a distributer flightcontroller. As used in this disclosure a “distributer flight controller”is a component that adjusts and/or controls a plurality of flightcomponents as a function of a plurality of flight controllers. Forexample, distributer flight controller may include a flight controllerthat communicates with a plurality of additional flight controllersand/or clusters of flight controllers. In an embodiment, distributedflight control may include one or more neural networks. For example,neural network also known as an artificial neural network, is a networkof “nodes,” or data structures having one or more inputs, one or moreoutputs, and a function determining outputs based on inputs. Such nodesmay be organized in a network, such as without limitation aconvolutional neural network, including an input layer of nodes, one ormore intermediate layers, and an output layer of nodes. Connectionsbetween nodes may be created via the process of “training” the network,in which elements from a training dataset are applied to the inputnodes, a suitable training algorithm (such as Levenberg-Marquardt,conjugate gradient, simulated annealing, or other algorithms) is thenused to adjust the connections and weights between nodes in adjacentlayers of the neural network to produce the desired values at the outputnodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 6, a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 6, flight controller may include asub-controller 640. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 604 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 640may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 640 may include any component of any flightcontroller as described above. Sub-controller 640 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 640may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 640 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 6, flight controller may include a co-controller644. As used in this disclosure a “co-controller” is a controller and/orcomponent that joins flight controller 604 as components and/or nodes ofa distributer flight controller as described above. For example, andwithout limitation, co-controller 644 may include one or morecontrollers and/or components that are similar to flight controller 604.As a further non-limiting example, co-controller 644 may include anycontroller and/or component that joins flight controller 604 todistributer flight controller. As a further non-limiting example,co-controller 644 may include one or more processors, logic componentsand/or computing devices capable of receiving, processing, and/ortransmitting data to and/or from flight controller 604 to distributedflight control system. Co-controller 644 may include any component ofany flight controller as described above. Co-controller 644 may beimplemented in any manner suitable for implementation of a flightcontroller as described above.

In an embodiment, and with continued reference to FIG. 6, flightcontroller 604 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 604 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 7, an exemplary embodiment of a machine-learningmodule 700 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 704 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 708 given data provided as inputs 712;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 7, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 704 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 704 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 704 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 704 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 704 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 704 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data704 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 7,training data 704 may include one or more elements that are notcategorized; that is, training data 704 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 704 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 704 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 704 used by machine-learning module 700 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample, sensor datum, defunct actuator, and any actuator performancemodel may be inputs and an actuator allocation command datum may be anoutput.

Further referring to FIG. 7, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 716. Training data classifier 716 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 700 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 704. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 716 may classify elements of training data to variouslevels of severity of a malfunction for one or more defunct actuatorsfor which a subset of training data may be selected.

Still referring to FIG. 7, machine-learning module 700 may be configuredto perform a lazy-learning process 720 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 704. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 704elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 7,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 724. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 724 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 724 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 704set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 7, machine-learning algorithms may include atleast a supervised machine-learning process 728. At least a supervisedmachine-learning process 728, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude a sensor datum, a defunct actuator, and any actuator performancemodel as inputs, actuator allocation command datum as an output, and ascoring function representing a desired form of relationship to bedetected between inputs and outputs; scoring function may, for instance,seek to maximize the probability that a given input and/or combinationof elements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 704. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 728 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 7, machine learning processes may include atleast an unsupervised machine-learning processes 732. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 7, machine-learning module 700 may be designedand configured to create a machine-learning model 724 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 7, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includevarious forms of latent space regularization such as variationalregularization. Machine-learning algorithms may include Gaussianprocesses such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 800 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 800 includes a processor 804 and a memory808 that communicate with each other, and with other components, via abus 812. Bus 812 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 804 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 804 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 804 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 816 (BIOS), including basic routines that help totransfer information between elements within computer system 800, suchas during start-up, may be stored in memory 808. Memory 808 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 820 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 808 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 800 may also include a storage device 824. Examples of astorage device (e.g., storage device 824) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 824 may be connected to bus 812 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 824 (or one or morecomponents thereof) may be removably interfaced with computer system 800(e.g., via an external port connector (not shown)). Particularly,storage device 824 and an associated machine-readable medium 828 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 800. In one example, software 820 may reside, completelyor partially, within machine-readable medium 828. In another example,software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In oneexample, a user of computer system 800 may enter commands and/or otherinformation into computer system 800 via input device 832. Examples ofan input device 832 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 832may be interfaced to bus 812 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 812, and any combinations thereof. Input device 832 mayinclude a touch screen interface that may be a part of or separate fromdisplay 836, discussed further below. Input device 832 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 800 via storage device 824 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 840. A network interfacedevice, such as network interface device 840, may be utilized forconnecting computer system 800 to one or more of a variety of networks,such as network 844, and one or more remote devices 848 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 844,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 820,etc.) may be communicated to and/or from computer system 800 via networkinterface device 840.

Computer system 800 may further include a video display adapter 852 forcommunicating a displayable image to a display device, such as displaydevice 836. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 852 and display device 836 may be utilized incombination with processor 804 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 800 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 812 via a peripheral interface 856. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods andsystems according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for flight control for managingactuators for an electric aircraft, the system comprising: at least atorque sensor configured to detect a sensor datum, wherein the sensordatum comprises an actuator torque datum from at least one actuator of aplurality of lift propulsor actuators on an electric aircraft; acontroller, wherein the controller is designed and configured to:generate an actuator performance model as a function of measured sensordata during at least a previous aircraft flight; receive the sensordatum from the at least a torque sensor; identify a defunct actuator ofthe plurality of lift propulsor actuators as a function of the sensordatum and the actuator performance model; generate an actuatorallocation command datum as a function of at least the actuatorperformance model and at least the identification of the defunctactuator, wherein the actuator allocation command datum comprises acommand for a torque allocation to be applied to one or more actuatorsof the plurality of actuators; and perform the torque allocation on theone or more actuators of the plurality of actuators as a function of theactuator allocation command datum.
 2. The system of claim 1, wherein thesensor datum further comprises an input datum from a manipulation of apilot input control and a flight datum describing logistical and/orphysical parameters of the electric aircraft.
 3. The system of claim 1,wherein the at least a sensor is disposed on at least an actuator of theelectric aircraft.
 4. The system of claim 1, wherein the controllerfurther comprises a flight simulator to simulate flight within anenvironment, wherein the flight simulator is configured to generate theactuator performance model.
 5. The system of claim 1, wherein thecontroller is further configured to generate an expected actuatorperformance model which embodies ideal actuator performance on theelectric aircraft.
 6. The system of claim 5, wherein the controller isfurther configured to compare the actuator performance model with theexpected actuator performance model to identify the defunct actuator. 7.The system of claim 1, wherein the controller further comprises an outerloop controller configured to generate a rate setpoint as a function ofthe sensor datum and the identification of the defunct actuator.
 8. Thesystem of claim 7, wherein the controller further comprises an innerloop controller configured to generate a moment datum as a function ofthe rate setpoint.
 9. The system of claim 8, wherein the controllerfurther comprises a mixer configured to at least generate the actuatorallocation command datum.
 10. The system of claim 1, wherein controlleris further configured to use a machine-learning model to generate theactuator allocation command datum as an output using the actuatorperformance model and at least the sensor datum as inputs.
 11. A methodfor flight control for managing actuators for an electric aircraft, themethod comprising: detecting, by a torque sensor, a sensor datumcomprising an actuator torque datum from at least one actuator of aplurality of lift propulsor actuators on an electric aircraft;generating, by a controller, an actuator performance model as a functionof measured sensor data during at least a previous aircraft flight;receiving, by a controller, a sensor datum from at least a torquesensor; identifying, by a controller, a defunct actuator of theplurality of lift propulsor actuators as a function of the sensor datumand the actuator performance model; generating, by a controller, anactuator allocation command datum as a function of at least the actuatorperformance model and at least the identification of the defunctactuator; and performing, by a controller, the torque allocation on theone or more actuators of the plurality of actuators as a function of theactuator allocation command datum.
 12. The method of claim 11, whereinreceiving the sensor datum further comprises receiving an input datumfrom a manipulation of a pilot input control and a flight datumdescribing logistical and/or physical parameters of the electricaircraft.
 13. The method of claim 11, wherein receiving the sensor datumfurther comprises the at least a sensor configured to be disposed on atleast an actuator of the electric aircraft.
 14. The method of claim 11,wherein the controller further comprises a flight simulator to simulateflight within an environment, wherein the flight simulator is configuredto generate the actuator performance model.
 15. The method of claim 14,wherein generating the actuator performance model further comprisesgenerating an expected actuator performance model as a function of theflight simulator, which embodies ideal actuator performance on theelectric aircraft.
 16. The method of claim 15, wherein identifying thedefunct actuator further comprises comparing the actuator performancemodel with the expected actuator performance model.
 17. The method ofclaim 11, wherein the method further comprises generating a ratesetpoint by an outer loop controller as a function of the sensor datumthe identification of the defunct actuator.
 18. The method of claim 17,wherein the method further comprises generating a moment datum, by aninner loop controller, as a function of the rate setpoint.
 19. Thesystem of claim 18, wherein the method further comprises generating theactuator allocation command datum by a mixer.
 20. The method of claim11, wherein generating the actuator allocation command further comprisesusing a machine-learning model to generate the actuator allocationcommand datum as an output using the actuator performance model and atleast the sensor datum as inputs.